JP5052107B2 - Voice reproduction device and voice reproduction method - Google Patents

Description

  The present invention relates to a sound reproduction device and a sound reproduction method for reproducing sound from in-body transmitted sound.

In recent years, the mainstream of person-to-person communication communication has shifted from mobile telephones using conventional fixed telephones to mobile communication using mobile telephones, and tens of millions of mobile telephones have become widespread in Japan alone. Also, as a method of mediating communication, not only simple voice transmission but also various information transmission techniques such as e-mails, photographs, and videophones have been developed one after another and are in widespread use.
However, since the main purpose of using mobile phones is person-to-person dialogues, there are many social adverse effects due to their spread. Mobile phone calls in quiet places such as libraries, loud calls late at night, on the train Calls such as can be said to be a nuisance. However, it is difficult to stop such acts in practice, as it is only an effort to raise awareness as an act contrary to social morals.
A trial of such a present situation, and a technique of “no voice call” based on the principle of speech recognition from body-conducted speech that can output (synthesize) speech without actually speaking is proposed (Non-Patent Document 1, 2).

With such a technique, it is possible in principle to reproduce and transmit sound from the body conduction sound.
Toda Tomomoto, Shikahiro Shikano, "Non-voice telephone", Proceedings of the National Meeting of the Acoustical Society of Japan, pp. 369-370, 3-6-21, 2005-9 Nakajima Yuki, Kashioka Hideki, Campbell Nick, Shikahiro Kano, "Non-audible tweet recognition", IEICE Transactions, Vol. J87-D-II, No. 9, pp. 1757-1764, 2004-9

  As described in "Background Art", in principle, it is possible to reproduce sound from body conduction sounds. However, in practice, there is a problem that it is difficult for the user to reproduce the voice of the individual user in an easy manner. The problems are shown below.

In order to use the above-described “voiceless phone” like a phone that is usually used, it is naturally necessary to generate a voice in which the individuality of the speaker is reproduced. In order to do so, it is necessary to collect a certain amount of individual voices and body conduction sounds for learning. However, during such voice collection, the user must utter a monotonous and uninteresting utterance for a long time. Such vocalization is generally not preferred. For this reason, since it is difficult to disseminate devices that require such procedures, there is a problem in that it is difficult to actually use such a learning method and it is difficult to reproduce an individual. Such a problem is common to the technology for reproducing sound from in-body conduction sound, and is not limited to the above-mentioned “voiceless telephone”.
The present invention has been made in view of these points, and an object of the present invention is to provide a technique capable of reproducing a user's personal voice from in-vivo transmitted sound without causing the user to perform complicated operations. And

  In the present invention, in order to solve the above-mentioned problems, a body conduction sound collection unit that collects body conduction sound caused by speech, a sound collection unit that collects speech, and a body conduction sound during speech with vocal cord movement Voice correspondence that synchronizes temporally the data of the body-transmitted sound collected by the sound collection unit and the data of the sound collected by the sound collection unit at the same time, and associates these with the body conduction sound A first transmission unit that transmits to the computer to learn the attached model, and further transmits to the computer the data of the body-transmitted sound collected by the body-conducted sound collection unit during speech without accompanying vocal cord movement, and vocal cord movement A first receiving unit that receives voice data generated by a computer using a voice association model with respect to in-vivo transmitted sound data collected by the in-body conduction sound collecting unit during utterance without speech A reproduction device is provided.

  The “internally transmitted sound due to speech” means sound transmitted by vibration of a human soft tissue caused by movement of a speech organ such as the periphery of the mouth or tongue during speech. In addition, the “body conduction sound caused by speech” includes body conduction sound caused by speech accompanied by vocal cord movement and body conduction sound caused by speech not accompanied by vocal cord movement. “Glottal movement” means movement that vibrates the vocal cords and narrows the glottis, and “utterance without vocal cord movement” means “Non-Audible Murmur (NAM)”. .

  Here, the speech reproduction device of the present invention collects normal speech by the sound collection unit during normal speech accompanied by vocal cord movement, and collects in-body conduction sound resulting from the utterance by the in-body conduction sound collection unit. Sound. Then, the sound reproduction device synchronizes temporally the sound data collected in this way and the data of the in-vivo transmission sound, and learns a sound association model that associates the conduction sound with the sound in the body. Send to the computer. The computer can learn the voice association model by using the time-synchronized voice data and the in-vivo transmission sound data. As described above, according to the present invention, learning data is collected only by data collected at the time of normal utterance. Therefore, the user does not need to perform a monotonous and uninteresting utterance in advance for collecting learning data for a long time. Further, in the present invention, learning is performed during a normal call that is generally considered to be most frequently used, so that model learning can be sufficiently performed without placing a burden on the learning work on the user. Note that “in-body conduction sound collection” is a sound generated by the movement of a speech organ such as the periphery of the mouth or tongue, and is hardly affected by whether or not the speech is accompanied by vocal cord movement. Therefore, there is no problem in using the feature value extracted from the body conduction sound collected during normal speech accompanied by vocal cord movement as learning data. On the contrary, in the present invention, since speech and in-vivo conduction sound collection are collected simultaneously from normal speech accompanied by vocal cord movement, it is more appropriate than collecting speech and in-body conduction sound collection separately. Learning data can be collected. In the present invention, since the voice and the body conduction sound are collected simultaneously, it is easy to synchronize them at the time of learning data collection. Therefore, the number of operations during learning can be reduced as compared with a case where these are statistically associated with each other during learning. Furthermore, the variance of the model increases due to an error in association, and as a result, the quality of reproduced speech is hardly degraded.

  In addition, the audio reproduction device of the present invention preferably further includes an audio reproduction unit that reproduces audio from audio data received by the first reception unit. Thereby, the sound reproduced when reproducing the utterance from the body conduction sound can be fed back to the speaker. Usually, when a person speaks, his / her speech is controlled by listening to his / her voice. In fact, even people with acquired hearing disabilities tend to have difficulty in speaking, and if they continue speaking in an environment where their voice cannot be opened for a long time, the speech may become very corrupted or the speech itself is difficult It becomes. By feeding back the sound reproduced from the body conduction sound to the user, the user can satisfactorily speak by the body conduction sound.

  In addition, the sound reproduction device of the present invention preferably further includes a bone conduction vibration unit that collects bone conduction sound due to speech and reproduces the bone conduction sound. The first transmission unit further transmits the data of the body conduction sound collected by the body conduction sound collection unit during speech with vocal cord movement and the bone conduction sound collected by the bone conduction vibration unit at the same time. The data is temporally synchronized, and these are transmitted to the computer in order to learn a bone conduction sound association model that correlates the body conduction sound and the bone conduction sound. In addition, the first receiving unit generates a bone conduction sound generated by the computer using the bone conduction sound association model with respect to the data of the body conduction sound collected by the body conduction sound collection unit during speech without accompanying vocal cord movement. The bone conduction vibration unit reproduces the bone conduction sound from the bone conduction sound data received by the first reception unit. In this case, the bone conduction sound reproduced from the body conduction sound collection can be fed back to the speaker.

  As described above, when only the sound reproduced from the body-transmitted sound is fed back to the speaker, the feedback sound is heard only from the speaker's ear. In this case, the speaker feels uncomfortable as if he / she listened to a recording of his / her speech. The reason for this is that humans usually listen to the inner ear by superimposing the voice spoken in the skull and the sound heard from the ear, and the sound heard only by the ear is different from the sound normally heard by the human. Because. On the other hand, in the preferable configuration of the present invention, since the bone conduction sound reproduced from the body conduction sound collection can be fed back to the speaker, the speaker can utter the body conduction sound without a sense of incongruity.

  In addition, in the case of a configuration in which only the voice is fed back to the speaker and listened, in some cases, the voice spoken by the speaker and the voice spoken by the other party overlap, making it difficult to listen to the other party's voice. Sometimes it ends up. On the other hand, such a problem does not occur in the configuration in which the bone conduction sound is fed back to the speaker, and smooth conversation is possible without obstructing the listening of the partner's speech. This is because the bone conduction sound does not confuse the voice of the other party.

  Also, in a very quiet environment such as a library, sound leakage from headphones may be a problem, and in such an environment, it is difficult to provide feedback of the sound reproduced from the body-transmitted sound. On the other hand, such a problem does not occur in the configuration in which the bone conduction sound is fed back to the speaker. Since the bone conduction sound does not cause sound leakage, the reproduced utterance can be fed back to the speaker without depending on the environment.

  In addition, in this configuration, the user does not need to make a monotonous and interesting utterance for a long time in order to collect learning data of the bone-conducted sound correspondence model, and is generally considered to be most frequently used during normal calls. Since learning is performed, model learning can be sufficiently performed without placing a burden on the learning work on the user. Furthermore, in this configuration, since the bone conduction sound and the body conduction sound collection sound are collected simultaneously from the normal utterance accompanied by the vocal cord movement, the bone conduction sound and the body conduction sound collection sound are collected separately. Therefore, it is easy to collect appropriate learning data and synchronize them in time.

The speech reproduction apparatus of the present invention preferably further includes an utterance state input unit that receives an input operation indicating whether the utterance is accompanied by vocal cord movement or the utterance does not involve vocal cord movement. When the input operation to the utterance state input unit indicates an utterance accompanied by vocal cord movement, the first transmission unit transmits the in-vivo transmission sound data collected by the in-body conduction sound collection unit and the voice at the same time. The voice data collected by the sound collection unit is sent to the computer in time synchronization, and the voice playback unit does not play back the voice, and the input operation to the speech state input unit does not involve vocal cord movement. In the case of indicating the time, the first transmission unit transmits only the data of the in-vivo transmission sound collected by the in-body conduction sound collection unit to the computer, and the first reception unit receives the audio data. The sound reproduction unit reproduces sound from the sound data. Note that “transmit only in-body transmission sound data to the computer” means that voice data and bone conduction sound data are not transmitted to the computer together with in-body transmission sound data. Transmitting only the data of internal body sound and control data together to the computer is also included in “send only body sound data to computer”.
As a result, it is possible to easily realize switching between model learning and sound reproduction from the body conduction sound.

The speech reproduction apparatus of the present invention preferably further includes an utterance state input unit that receives an input operation indicating whether the utterance is accompanied by vocal cord movement or the utterance does not involve vocal cord movement. When the input operation to the utterance state input unit indicates an utterance accompanied by vocal cord movement, the first transmission unit transmits the data of the body-transmitted sound collected by the body conduction sound collection unit and the bone at the same time. The bone conduction sound data collected by the conduction vibration unit is temporally synchronized and transmitted to the computer. The bone conduction vibration unit does not reproduce the bone conduction sound, and the input operation to the utterance state input unit is a vocal cord. In the case of indicating an utterance without movement, the first transmission unit transmits only the data of the in-vivo transmission sound collected by the internal conduction sound collection unit to the computer, and the first reception unit The bone conduction vibration unit receives the sound conduction data and reproduces the bone conduction sound from the bone conduction sound data.
As a result, it is possible to easily realize switching between model learning and sound reproduction from the body conduction sound.

  In the sound reproduction device of the present invention, preferably, the computer uses the second reception unit that receives data transmitted from the sound reproduction device and the data of the in-vivo transmission sound to extract the feature amount of the in-body transmission sound. The first feature quantity extraction unit, the second feature quantity extraction unit that extracts voice feature quantity using voice data, and the temporally synchronized in-vivo transmitted sound feature quantity and voice feature quantity correspond to each other A learning unit that calculates a parameter of a speech association model indicating a correspondence relationship between a feature amount of an arbitrary in-vivo transmitted sound and a feature amount of an arbitrary speech, and a speech learning unit. The voice association model application unit for calculating the feature amount of the voice corresponding to the feature amount of the in-vivo transmission sound, and the voice of the voice calculated by the voice association model application unit Using features, audio And a second transmission unit that transmits the audio data generated by the audio restoration unit to the audio reproduction device, and the second reception unit synchronizes with time. When the data and the voice data are received, the first feature amount extraction unit extracts the feature amount of the in-vivo transmission sound using the in-vivo transmission sound data received by the second reception unit, and the second feature amount extraction unit Uses the speech data received by the second receiving unit to extract speech feature values, the speech learning unit uses these feature values to calculate parameters of the speech association model, and the second receiving unit When only the in-body transmission sound data is received, the first feature amount extraction unit extracts the in-body transmission sound feature amount using the in-body transmission sound data received by the second reception unit, and the speech association model application unit Is the extracted feature value of the internal transmission sound and the parameter calculated by the speech learning unit. The voice feature amount corresponding to the feature amount of the in-vivo transmitted sound is calculated, and the voice restoration unit generates voice data using the calculated voice feature amount, and the second transmission unit Transmits the voice data generated by the voice restoration unit to the voice reproduction device.

  Here, the computer performs model learning processing and model application depending on whether the second receiving unit has received time-synchronized in-vivo transmission sound data and audio data or only in-body transmission sound data. Switching between processing. Thus, the model learning process and the model application process can be appropriately switched according to the utterance state of the speaker without performing complicated processes in the voice reproduction device.

  In the sound reproduction device of the present invention, preferably, the computer uses the second reception unit that receives data transmitted from the sound reproduction device and the data of the in-vivo transmission sound to extract the feature amount of the in-body transmission sound. A first feature quantity extraction unit; a third feature quantity extraction unit that extracts bone conduction sound feature data using bone conduction sound data; and a time-synchronized in-body transmission sound feature quantity and bone conduction sound feature Bones that calculate the parameters of the bone conduction sound correlation model that indicates the correspondence between the feature quantities of any body-borne sound and any bone conduction sound by learning processing. Use bone conduction sound to calculate bone conduction sound feature quantity corresponding to body conduction sound feature quantity using parameters calculated by bone conduction learning section, bone conduction sound learning section and body conduction sound feature quantity Bone conduction sound calculated by the attachment model application unit and the bone conduction sound matching model application unit A bone conduction sound restoration unit that generates bone conduction sound data using the collected amount, and a second transmission unit that transmits the bone conduction sound data generated by the bone conduction sound restoration unit to the sound reproduction device. When the second reception unit receives the time-synchronized in-body transmission sound data and bone conduction sound data, the first feature amount extraction unit uses the in-body transmission sound data received by the second reception unit. The third feature amount extraction unit extracts the bone conduction sound feature amount using the bone conduction sound data received by the second reception unit, and the bone conduction sound learning unit When the parameters of the bone conduction sound association model are calculated using these feature amounts, and the second reception unit receives only the data of the in-vivo transmission sound, the first feature amount extraction unit receives the second reception unit. The feature value of the body conduction sound is extracted using the data of the body conduction sound thus obtained, and the bone conduction sound matching model application unit Using the feature amount of the reaching sound and the parameter calculated by the bone conduction sound learning unit, the feature amount of the bone conduction sound corresponding to the feature amount of the in-vivo transmission sound is calculated, and the bone conduction sound restoration unit is calculated The bone conduction sound data is generated using the feature value of the bone conduction sound, and the second transmission unit transmits the bone conduction sound data generated by the bone conduction sound restoration unit to the sound reproducing device.

  Here, the computer performs model learning processing depending on whether the second receiving unit receives the data of the body conduction sound and the data of the bone conduction sound that are synchronized in time or only the data of the body conduction sound. Switching between model application processing. Thus, the model learning process and the model application process can be appropriately switched according to the utterance state of the speaker without performing complicated processes in the voice reproduction device.

  As described above, according to the present invention, it is possible to reproduce the user's personal voice from the in-body transmitted sound without causing the user to perform complicated work.

The best mode for carrying out the present invention will be described below with reference to the drawings.
[First Embodiment]
The first embodiment of the present invention will be described below.
<Configuration>
FIG. 1A is a conceptual diagram illustrating the configuration of the entire system of this embodiment.

  As illustrated in FIG. 1A, the system according to the present embodiment includes an audio reproduction device 10 and a computer 20, which are connected to each other via a connection line 30 so as to communicate with each other in electrical signals. The sound reproduction apparatus 10 includes a speaker 11 such as a headphone or an earphone for reproducing sound reproduced from the body conduction sound (corresponding to a “sound reproduction unit”), and a body conduction sound microphone for collecting the body conduction sound. 12 (corresponding to “in-body conduction sound collecting unit”), a sound collecting microphone 13 (corresponding to “sound collecting unit”) for collecting normal sound, a main body 14 electrically connected thereto, and It has a changeover switch 14a (corresponding to “speech state input unit”) that receives an input operation indicating whether the speech is accompanied by vocal cord movement or the speech does not involve vocal cord movement.

  Here, the body conduction sound microphone 12 is, for example, a body surface-adhesive stethoscope-type microphone described in Non-Patent Document 2. In order to collect the optimal body conduction sound, a part of the upper part of the diaphragm of the body conduction sound microphone 12 is a “milky process” immediately behind the ear canal of the speaker's skull base. It is desirable to attach so that it may cover the bone part called (refer nonpatent literature 2). Further, the computer 20 may be a general PC (Personal Computer) composed of a CPU (Central Processing Unit), a RAM (Random-Access Memory), or the like, or a mobile phone or PDA (Personal Computer) incorporating a CPU, a RAM, or the like. A portable device such as a digital assistant), or a device capable of calculation processing dedicated to this embodiment. Further, the sound reproduction device 10 and the computer 20 may be configured in separate casings, or may be configured in the same casing. Moreover, what is necessary is just to use the thing corresponding to the transmission format of an audio | voice and body conduction audio | voices, such as an audio | voice cable, an optical fiber, and a network cable, for the connection line 30. The audio reproduction device 10 and the computer 20 may be simply connected digitally or may be connected through a network connection device such as a modem. The audio reproduction device 10 may be configured not to perform D / A conversion or A / D conversion, and the audio reproduction device 10 and the computer 20 may be connected via a D / A converter or an A / D converter. .

FIG. 1B is a conceptual diagram illustrating the configuration of the main body 14 of this embodiment.
As illustrated in FIG. 1B, the main body 14 of this embodiment includes a changeover switch 14a, a control unit 14b, switches 14c and 14d, A / D converters 14e and 14f, a synchronization unit 14g, and a D / A converter 14h. , An amplifier 14i, a transmission unit 14j, and a reception unit 14k. The control unit 14b and the synchronization unit 14g are configured, for example, by reading a predetermined program into a known computer. The transmission unit 14j and the reception unit 14k are communication devices (for example, a network card, an optical transmission module, etc.) corresponding to the transmission format. The A / D converters 14e and 14f may be one physical circuit.

As illustrated in FIG. 1B, the receiving unit 14k is electrically connected to the D / A converter 14h via the switch 14c, and the D / A converter 14h is electrically connected to the speaker 11 via the amplifier 14i. Connected to. The transmission unit 14j is connected to the synchronization unit 14g. The synchronization unit 14g is electrically connected to the sound collection microphone 13 via the switch 14d and the A / D converter 14f, and electrically connected to the in-body conduction sound microphone 12 via the A / D converter 14e. Connected. The changeover switch 14a is configured such that the output signal can be input to the control unit 14b, and the control unit 14b is configured to be able to supply a control signal to the switches 14c and 14d. The main body 14 executes each process under the control of the control unit 14b.
Also, a predetermined program is read into the computer 20 of this embodiment, and each functional configuration is realized by the CPU executing the program. FIG. 2 is a block diagram illustrating a functional configuration of the computer 20 of the present embodiment realized in this way.

  As illustrated in FIG. 2, the computer 20 of the present embodiment includes a receiving unit 20 a, a determining unit 20 b, a first feature amount extracting unit 20 c, a second feature amount extracting unit 20 d, storage units 20 e and 20 g, a speech learning unit 20 f, A voice association model application unit 20h, a voice restoration unit 20i, a transmission unit 20j, a temporary memory 20k, and a control unit 20m are provided. Here, the receiving unit 20a and the transmitting unit 20j are communication devices compatible with a transmission format driven under the control of the CPU, and the storage units 20e and 20g and the temporary memory 20k are, for example, a RAM, a register, a hard disk, or the like. This is a composite storage area. Further, the determination unit 20b, the first feature quantity extraction unit 20c, the second feature quantity extraction unit 20d, the speech learning unit 20f, the speech association model application unit 20h, the speech restoration unit 20i, and the control unit 20m are predetermined on the CPU. It is configured by executing a program. The computer 20 executes each process under the control of the control unit 20m. Unless otherwise specified, the data calculated by each process is temporarily stored in the temporary memory 20k and read out as necessary.

<Operation of the sound reproduction device 10>
Next, the operation of the sound reproduction device 10 of this embodiment will be described.
The user switches the changeover switch 14a according to whether he / she performs a normal utterance with vocal cord movement or an utterance without vocal cord vibration. The switching state of the changeover switch 14a is input to the control unit 14b as an electrical signal. The control unit 14b controls the switches 14c and 14d as follows according to the switching state of the changeover switch 14a indicated by the input electrical signal.

すなわち、切り替えスイッチ１４ａが声帯運動を伴う発話を示す状態にスイッチングされた場合、制御部１４ｂは、スイッチ１４ｃをＯＦＦにし、スイッチ１４ｄをＯＮとする。一方、切り替えスイッチ１４ａが声帯運動を伴わない発話を示す状態にスイッチングされた場合、制御部１４ｂは、スイッチ１４ｃをＯＮにし、スイッチ１４ｄをＯＦＦとする。このような状態において、声帯運動を伴う発話又は声帯運動を伴わない発話が行われると、音声再現装置１０は以下のように動作する。

In other words, when the changeover switch 14a is switched to a state indicating speech with vocal cord movement, the control unit 14b turns off the switch 14c and turns on the switch 14d. On the other hand, when the changeover switch 14a is switched to a state indicating an utterance not accompanied by vocal cord movement, the control unit 14b turns on the switch 14c and turns off the switch 14d. In such a state, when an utterance accompanied by vocal cord movement or an utterance not accompanied by vocal cord movement is performed, the voice reproduction device 10 operates as follows.

[Operation when speaking with vocal cord movement (switch 14c: OFF, switch 14d: ON)]
When the speaker utters with vocal cord movement, the normal voice uttered is collected by the voice collecting microphone 13 and converted into an analog electric signal. At the same time, the in-body transmission sound accompanying this utterance is collected by the in-body conduction sound microphone 12 and converted into an analog electric signal. The collected analog electrical signal of internal transmission sound and audio analog electrical signal are converted into digital electrical signals by A / D converters 14e and 14f, respectively, and input to the synchronization unit 14g. The synchronization unit 14g temporally synchronizes the digital electrical signal of the internal transmission sound and the audio digital electrical signal, and sends them to the transmission unit 14j. This synchronization is performed, for example, by associating the digital electric signal of the body-transmitted sound and the digital electric signal of the sound every discrete time in the input order. Also, control data such as data indicating the type of the signal is added to the digital electrical signal of the internal transmission sound and the audio digital electrical signal synchronized in time under the control of the control unit 14b. The The transmission unit 14j transmits the transmitted digital electrical signal to the computer 20 via the connection line 30 as digital data or as network data based on a specific protocol. The audio reproduction device 10 does not include the A / D converters 14e and 14f, and the computer 20 receives the analog electrical signal of the collected body-borne sound and the analog electrical signal that is temporally synchronized with the analog analog signal of the speech. It is good also as a structure which transmits to. In this case, the computer 20 performs conversion from an analog electrical signal to a digital signal.

  The data transmitted to the computer 20 in this manner is used for the computer 20 to learn a voice association model that associates the body conduction sound with the voice. The processing of the computer 20 will be described later. In addition, when the changeover switch 14a is switched to a state indicating an utterance accompanied by vocal cord movement, the control unit 14b does not cause the speaker 11 or the bone conduction vibrator 115 to perform a reproduction operation.

[Operation when speaking without vocal cord movement (switch 14c: ON, switch 14d: OFF)]
When a speaker performs an utterance without accompanying vocal cord movement, the in-body transmission sound accompanying the utterance is collected by the in-body conduction sound microphone 12 and converted into an analog electric signal. The collected body-transmitted sound is converted into a digital electric signal by the A / D converter 14f and input to the synchronization unit 14g. Under the control of the control unit 14b, the synchronization unit 14g adds control data such as data indicating the type of the signal to the digital electrical signal of the in-vivo transmission sound and sends the data to the transmission unit 14j. The transmission unit 14j transmits the transmitted digital electrical signal to the computer 20 via the connection line 30 as digital data or as network data based on a specific protocol. The collected analog electrical signal of the in-vivo transmission sound may be transmitted to the computer 20 as it is. In this case, the computer 20 performs conversion from an analog electric signal to a digital electric signal.

  The computer 20 generates voice data using the voice association model for the transmitted body sound, and transmits the generated voice data to the voice reproduction device 10 as a digital electrical signal (of the computer 20). The operation will be described later). The audio digital electric signal transmitted from the computer 20 is received by the receiving unit 14k of the audio reproduction device 10, and is input to the D / A converter 14h via the switch 14c. The D / A converter 14 h converts the input digital audio signal into an analog signal and inputs the analog signal to the speaker 11. The speaker 11 reproduces the sound based on the analog signal of the input sound at a volume set by the user or set in advance. In the case of a configuration in which an audio analog signal is transmitted from the computer 20, the D / A converter 14h is not necessary, and the speaker 11 performs audio reproduction from the transmitted audio analog signal.

<Operation of computer 20>
Next, the operation of the computer 20 will be exemplified.
FIG. 3 is a flowchart for explaining the operation of the computer 20 of this embodiment. Hereinafter, the operation of the computer 20 of this embodiment will be exemplified according to this figure.
First, the electrical signal transmitted from the audio reproduction device 10 is received by the receiving unit 20a (step S1). The received electrical signal is converted into data suitable for processing by the computer 20 as necessary, and the converted data is buffered in the temporary memory 20k and sent to the determination unit 20b in units of frames, for example.

  The determination unit 20b determines whether or not the received reception data includes audio data (step S2). This determination is performed by, for example, the determination unit 20b with reference to control data included in the reception data. If it is determined that the received data includes audio data, the following steps S3 to S6 are executed. If it is determined that the received data does not include audio data, the following step S7 is performed. To S10 are executed. This control is performed by the control unit 20m.

[When it is determined that the received data includes audio data (S3 to S6)]
First, the first feature quantity extraction unit 20c analyzes the in-vivo transmission sound data transferred from the determination unit 20b and determines the feature quantity [Y _j (j = 1,..., J, J Is a natural number)] (step S3). In addition, the second feature amount extraction unit 20d analyzes the voice data transferred from the determination unit 20b (the voice data synchronized in time with the in-body transmission sound data in step S3), and the voice feature amount (X _j ) is extracted (step S4). Examples of the feature amount extracted here include a spectrum, a fundamental frequency, a sound source component, an aperiodic component, and dynamic features thereof (primary time difference, secondary time difference) and the like. As the analysis method, for example, a known method such as an LPC analysis method, a cepstrum analysis method, or a STRAIGHT analysis method is used. The natural number J indicates the number of types of feature values to be extracted, and j is an identifier corresponding to the type of feature values to be extracted.
Next, the in-vivo transmission sound feature value (Y _j ) extracted by the first feature value extraction unit 20c and the sound feature value (X _j ) extracted by the second feature value extraction unit 20d are, for example, The data is stored in the storage unit 20e in association with each other as a predetermined time interval (step S5).

Next, the speech learning unit 20f reads from the storage unit 20e the feature values (Y _j ) and the sound feature values (X _j ) of the in-vivo transmission sound that are associated with each other, and learning data that corresponds to these features. Then, by the learning process, a parameter of the voice association model indicating the correspondence relationship between the feature quantity of any in-body transmitted sound and the feature quantity of any voice is calculated (step S6). This process is executed for each type of feature quantity (for each j), and the calculated parameters of the voice association model are stored in the storage unit 20g for each j. As the voice association model, for example, a mixed normal distribution model (GMM: Gaussian Mixture Model) can be used as in Non-Patent Document 1. However, in the method of Non-Patent Document 1, both feature quantities are associated using dynamic programming, but in the case of this embodiment, both feature quantities are data synchronized in time, No association by the programming method is necessary. This is also a feature of this embodiment. That is, in the case of this embodiment, since the association of both feature quantities by dynamic programming is not necessary, the effect that the calculation time in the computer 20 can be shortened, and the variance of the model increases due to the association error, and the result In particular, there is an effect that the problem of causing the quality deterioration of the reproduced voice is not generated. Note that step S6 is not necessarily executed every time, and may be executed every time a predetermined number of feature amounts X _j and Y _j are collected.

ここで、特徴量Ｘ_ｊ，Ｙ_ｊはそれぞれベクトルであり、p_j(X_ｊ ,Y_ｊ)は特徴量Ｘ_ｊ，Ｙ_ｊの結合確率密度であり、ＭはＧＭＭを構成する正規分布の数を示す自然数であり、N(X_ｊ, Y_ｊ ; M_m ^j, Σ_m ^j)はｍ（m=1,...,M）番目の正規分布を意味し、λ_ｍ ^jはｍ番目の正規分布N(X_ｊ, Y_ｊ ; M_m ^j, Σ_m ^j)の重みを意味する。また、Ｍ_ｍ ^jは、特徴量Ｘ_ｊのｍ番目の平均ベクトルμ_ｍ ^Ｘjと、特徴量Ｙ_ｊのｍ番目の平均ベクトルμ_ｍ ^Ｙjとの集合を示す。また、Σ_ｍは、平均ベクトルμ_ｍ ^Ｘjに対応する特徴量Ｘ_ｊの共分散行列Σ_ｍ ^Ｘjと、平均ベクトルμ_ｍ ^Ｙjに対応する特徴量Ｙ_ｊの共分散行列Σ_ｍ ^Ｙjと、平均ベクトルμ_ｍ ^Ｘjに対応する特徴量Ｘ_ｊと平均ベクトルμ_ｍ ^Ｙjに対応する特徴量Ｙ_ｊとの組に対する共分散行列Σ_ｍ ^ＹjＸjと、の集合を示す。 [Specific examples of model learning]
Specific examples of model learning are shown below.
The voice association model of this specific example is the following GMM.

Here, the feature amounts X _j and Y _j are vectors, respectively, p _j (X _j , Y _j ) is the joint probability density of the feature amounts X _j and Y _j , and M is the number of normal distributions constituting the GMM N (X _j , Y _j ; M _m ^j , Σ _m ^j ) means the mth (m = 1, ..., M) normal distribution, and λ _m ^j is the mth It means the weight of the normal distribution N (X _j , Y _j ; M _m ^j , Σ _m ^j ). Further, _{M m} ^j denotes the m-th mean vector mu _m ^Xj feature quantity _{X j,} a set of m-th mean vector mu _m ^Yj feature amount _{Y j.} Furthermore, sigma _m is a covariance matrix sigma _m ^Xj feature quantity _{X j} which corresponds to the mean vector mu _m ^Xj, and covariance matrix sigma _m ^Yj feature amount _{Y j} corresponding to the average vector mu _m ^Yj, the average vector It shows the covariance matrix Σ _m ^YjXj for the set of the feature amount _{Y j} corresponding to the feature quantity _{X j} and the average vector mu _m ^Yj corresponding to mu _m ^Xj, a set of.

In addition, it is common to use a well-known EM algorithm for GMM learning (for example, K. Tokuda, T. Yoshimura, T. Masuko, T. Kobayashi and T. Kitamura, “Speech parameter generation algorithm for HMM”). -based speech synthesis ", Proc. ICASSP'2000, pp. 1315-1318). A GMM learning method using the EM algorithm will be exemplified below.
First, the following relationships can be defined for the feature amounts X _j and Y _j .

ここでＥは期待値であり、Ｗ_ｊ ^ｍはｍ番目の変換行列であり、ｂ_ｊ ^ｍはｍ番目のバイアス定数である。なお、P(c_m|Y_j)は、ｍ番目の正規分布N(X_ｊ, Y_ｊ ; M_m ^ｊ, Σ_m ^ｊ)からの特徴量Y_ｊの出現確率であり、下記のように示される。

Here, E is an expected value, W _j ^m is the m-th transformation matrix, and b _j ^m is the m-th bias constant. P (c _m | Y _j ) is an appearance probability of the feature quantity Y _j from the m-th normal distribution N (X _j , Y _j ; M _m ^j , Σ _m ^j ), and is expressed as follows: It is.

このとき、ＥＭアルゴリズムのＱ関数を以下のようにする。

At this time, the Q function of the EM algorithm is as follows.

そして、ＥＭアルゴリズムのＥステップでは、パラメータλ_m ^j，Ｍ_ｍ ^j，Σ_ｍ ^jに対して式（４）の計算を行い、Ｑを算出する。なお、パラメータＭ_ｍ ^jは、学習データである複数の特徴量Ｘ_ｊ，Ｙ_ｊに対して決定される値である（どの特徴量Ｘ_ｊ，Ｙ_ｊがどのｍに対応するかは未知）。また、パラメータＭ_ｍ ^jが決定されればパラメータΣ_ｍ ^jも定まる。

Then, in the E step of the EM algorithm, Q is calculated by calculating the equation (4) for the parameters λ _m ^j , M _m ^j , and Σ _m ^j . The parameter M _m ^j is a value determined for a plurality of feature amounts X _j , Y _j as learning data (which feature amount X _j , Y _j corresponds to which m is unknown). If the parameter M _m ^j is determined, the parameter Σ _m ^j is also determined.

In the M step of the EM algorithm, the parameter λ _m ^j for maximizing Q calculated by the equation (4) substituted with the parameters M _m ^j and Σ _m ^j used in the E step is used in the next E step. The value of the parameter λ _m ^j . Specifically, for example, the parameters M _m ^j and Σ _m ^j used in the E step are substituted, and the equation (4) with the parameter λ _m ^j as a variable is partially differentiated by λ _m ^j , and the partial differentiation result thereof There 0 become lambda _m ^j to the value of the parameter lambda _m ^j in the next E-step. Further, in this M step, in the formula (4) in which λ _m ^j used in the E step is substituted, the parameters M _m ^j , Σ _m ^j are used for a plurality of feature amounts X _j , Y _{j as} learning data. Parameters M _m ^j and Σ _m ^j are determined so that Q is maximized. The determination of the parameters M _m ^j and Σ _m ^j is performed by, for example, partial differentiation of a variable related to the parameter M _m ^j . That is, first, the parameters M _m ^j and Σ _m ^j are expressed by a function F that associates a plurality of feature amounts X _j and Y _j that are learning data with m. Then, the equation (4) in which the parameters M _m ^j and Σ _m ^j expressed as described above and λ _m ^j used in the E step is substituted is partially differentiated by the variable of the function F, and the partial differentiation result is 0. And the parameters M _m ^j and Σ _m ^j in the next E step are determined.

Then, the E step and the M step as described above are repeated until Q converges to a predetermined range, and the parameters λ _m ^j , M _m ^j , and Σ _m ^j when Q converges to the predetermined range are changed as model parameters of the GMM. Determine as.
Further, when the parameters λ _m ^j , M _m ^j , and Σ _m ^j are determined, if the equations (2) and (4) are collated, the transformation matrix W _j ^m and the bias constant b _j ^m are obtained.
W _j ^m = Σ _m ^YjXj・ (Σ _m ^Yj ) ^-1 (5)
b _j ^m = μ _m ^Xj ‐Σ _m ^YjXj・ (Σ _m ^Yj ) ⁻¹・ μ _m ^Yj … (6)
(End of explanation of [Specific example of model learning]).

[When it is determined that the received data does not include audio data (S7 to S10)]
First, the first feature amount extraction unit 20c analyzes the in-vivo transmission sound data transferred from the determination unit 20b, and extracts the feature amount (Y _j ′) of the in-body transmission sound (step S7). The extracted feature quantity (Y _j ′) of the in-vivo transmission sound is transferred to the voice association model application unit 20h. The speech association model application unit 20h uses the parameters of the speech association model read from the storage unit 20g and the feature amount (Y _j ′) of the in-vivo transmission sound, and uses the feature amount (Y _j of the in-vivo transmission sound) for each _j. An audio feature quantity (X _j ′) corresponding to “)” is calculated (step S8). Specifically, for example, the calculation results of the above formulas (5) and (6) and the feature value (Y _j ′) of the in-vivo transmission sound are substituted into the formula (2), and the calculation result is set as X _j ′. .

The calculated speech feature quantity (X _j ′) is transferred to the speech restoration unit 20i, and the speech restoration unit 20i synthesizes a speech waveform from the speech feature quantity (X _j ′) to generate speech data (step S9). ). The synthesis of the speech waveform data is performed by a synthesis method that is paired with the analysis method for extracting the feature value (X _j ′). For example, if the second feature quantity extraction unit 20d is configured to extract feature quantities by the LPC analysis method, the speech restoration unit 20i performs synthesis by the LPC synthesis method. Further, if the second feature quantity extraction unit 20d is configured to extract feature quantities by the cepstrum analysis method, the speech restoration unit 20i performs synthesis by the cepstrum synthesis method. If the second feature quantity extraction unit 20d is configured to extract feature quantities by the STRAIGHT analysis method, the voice restoration unit 20i performs synthesis by the STRAIGHT synthesis method.
The audio data generated by the audio restoration unit 20i is transmitted from the transmission unit 20j to the audio reproduction device 10, and is reproduced by the speaker 11 of the audio reproduction device 10 as described above.

[Second Embodiment]
Next, another embodiment of the present invention will be described. In this embodiment, not only a voice but also a bone conduction sound is reproduced from the body conduction sound. Below, it demonstrates centering around difference with 1st Embodiment, and simplifies description about the matter which is common in 1st Embodiment.

<Configuration>
FIG. 4 is a conceptual diagram illustrating the configuration of the entire system of this embodiment. In FIG. 4, the same reference numerals as those in FIG. 1A are assigned to portions common to the first embodiment.
As illustrated in FIG. 4, the system according to the present embodiment includes an audio reproduction device 110 and a computer 120, which are connected to each other via a connection line 30 in an electrical signal manner. The sound reproduction device 110 includes a bone-conducting vibrator (corresponding to a “bone-conducting vibration unit”) that collects and reproduces bone-conducted sound resulting from speech, and a speaker 11 (corresponding to a “sound reproduction unit”). In-body conduction sound microphone 12 (corresponding to “in-body conduction sound collection unit”), voice collection microphone 13 (corresponding to “sound collection unit”), main body 14 and changeover switch 14a (“speech state input unit”) Equivalent). As in the first embodiment, the computer 120 may be a general PC, a portable device incorporating a CPU, a RAM, or the like, or a device capable of calculation processing dedicated to this embodiment. May be. Further, the sound reproduction device 110 and the computer 120 may be configured in separate casings, or may be configured in the same casing. Further, the audio reproduction device 110 may be configured not to perform D / A conversion or A / D conversion, and the audio reproduction device 110 and the computer 120 may be connected via a D / A converter or an A / D converter. .

FIG. 5 is a conceptual diagram illustrating the configuration of the main body 114 of this embodiment. In FIG. 5, the same reference numerals as those in FIG.
As illustrated in FIG. 5, the main body 114 of this embodiment includes a changeover switch 14a, a control unit 14b, switches 14c, 14d, 114a, 114b, A / D converters 14e, 14f, 114c, a synchronization unit 14g, and a D / A. Converters 14h and 114b, an amplifier 14i, a transmission unit 14j, and a reception unit 14k are provided. The A / D converters 14e, 14f, and 114c may be one physical circuit, and the D / A converters 14h and 114b may be one physical circuit. Good.

  As illustrated in FIG. 5, the receiving unit 14k is electrically connected to the D / A converter 114d via the switch 114b, and the D / A converter 114d is electrically connected to the bone-conducting vibrator 115. . The receiving unit 14k is electrically connected to the D / A converter 14h via the switch 14c, and the D / A converter 14h is electrically connected to the speaker 11 via the amplifier 14i. The transmission unit 14j is connected to the synchronization unit 14g. The synchronization unit 14g is electrically connected to the bone conduction vibrator 115 via the switch 114a and the A / D converter 114c, and connected to the sound collection microphone 13 via the switch 14d and the A / D converter 14f. It is electrically connected and is electrically connected to the body conduction sound microphone 12 via the A / D converter 14e. Further, the changeover switch 14a is configured such that the output signal can be input to the control unit 14b, and the control unit 14b is configured to be able to supply a control signal to the switches 14c, 14d, 114a, and 114b. The main body 114 executes each process under the control of the control unit 14b.

  In addition, a predetermined program is read into the computer 120 of this embodiment, and each functional configuration is realized by the CPU executing the program. FIG. 6 is a block diagram illustrating a functional configuration of the computer 120 of the present embodiment realized in this way. In FIG. 6, the same reference numerals as those in FIG.

  As illustrated in FIG. 6, the computer 120 according to the present exemplary embodiment includes a receiving unit 20a, a determination unit 20b, a first feature quantity extraction unit 20c, a second feature quantity extraction unit 20d, a third feature quantity extraction unit 120d, and a storage unit 120e. 120g, speech learning unit 20f, bone conduction sound learning unit 120f, speech association model application unit 20h, bone conduction association model application unit 120h, speech restoration unit 20i, bone conduction sound restoration unit 120i, transmission unit 20j, temporary A memory 20k and a control unit 20m are provided. Here, the third feature quantity extraction unit 120d, the bone conduction sound learning unit 120f, the bone conduction sound association model application unit 120h, and the bone conduction sound restoration unit 120i are configured by executing a predetermined program on the CPU. Is. The computer 120 executes each process under the control of the control unit 20m.

<Operation of the sound reproduction device 110>
Next, the operation of the sound reproduction device 110 of this embodiment will be described.
The user switches the changeover switch 14a according to whether he / she performs a normal utterance with vocal cord movement or an utterance without vocal cord vibration. The switching state of the changeover switch 14a is input to the control unit 14b as an electrical signal. The control unit 14b controls the switches 14c, 14d, 114a, and 114b as follows according to the switching state of the changeover switch 14a indicated by the input electric signal.

すなわち、切り替えスイッチ１４ａが声帯運動を伴う発話を示す状態にスイッチングされた場合、制御部１４ｂは、スイッチ１４ｃをＯＦＦにし、スイッチ１４ｄをＯＮとし、スイッチ１１４ａをＯＮにし、スイッチ１１４ｂをＯＦＦとする。一方、切り替えスイッチ１４ａが声帯運動を伴わない発話を示す状態にスイッチングされた場合、制御部１４ｂは、スイッチ１４ｃをＯＮにし、スイッチ１４ｄをＯＦＦとし、スイッチ１１４ａをＯＦＦにし、スイッチ１１４ｂをＯＮとする。このような状態において、声帯運動を伴う発話又は声帯運動を伴わない発話が行われると、音声再現装置１１０は以下のように動作する。

That is, when the changeover switch 14a is switched to a state that indicates speech accompanied by vocal cord movement, the control unit 14b turns off the switch 14c, turns on the switch 14d, turns on the switch 114a, and turns off the switch 114b. On the other hand, when the changeover switch 14a is switched to a state that indicates an utterance without vocal cord movement, the control unit 14b turns on the switch 14c, turns off the switch 14d, turns off the switch 114a, and turns on the switch 114b. . In such a state, when an utterance with vocal cord movement or an utterance without vocal cord movement is performed, the voice reproduction device 110 operates as follows.

[Operation during utterance with vocal cord movement (switch 14c: OFF, switch 14d: ON, switch 114a: ON, switch 114b: OFF)]
When the speaker utters with vocal cord movement, the normal voice uttered is collected by the voice collecting microphone 13 and converted into an analog electric signal. At the same time, the bone conduction sound accompanying this utterance is collected by the bone conduction vibrator 115 and converted into an analog electrical signal, and the internal body transmission sound is collected by the body conduction sound microphone 12 and converted into an analog electrical signal. . The analog electrical signal of the body-borne sound, the analog electrical signal of the bone conduction sound, and the analog electrical signal of the voice are converted into digital electrical signals by the A / D converters 14e, 114c, and 14f, respectively, and input to the synchronization unit 14g. . The synchronization unit 14g temporally synchronizes the digital electrical signal of the body-transmitted sound, the digital electrical signal of the bone conduction sound, and the digital electrical signal of the sound, and sends them to the transmission unit 14j. Note that, as in the first embodiment, control signals such as data indicating the type of the signal are also added to these signals under the control of the control unit 14b. The transmitter 14j transmits the transmitted digital electrical signal to the computer 120 via the connection line 30 as digital data or as network data based on a specific protocol. Note that, as in the modification described in the first embodiment, the collected body-borne sound, bone conduction sound, and sound may be transmitted to the computer 120 as analog signals. In this case, the computer 120 performs conversion from an analog electrical signal to a digital signal.

  The data transmitted to the computer 120 in this way is used for the computer 120 to learn a speech association model that associates the body conduction sound and the speech and a bone conduction sound association model that associates the body conduction sound and the bone homophone. . The processing of the computer 120 will be described later. In addition, when the changeover switch 14a is switched to a state indicating utterance accompanied by vocal cord movement, the control unit 14b does not cause the speaker 11 to perform a reproduction operation.

[Operation during utterance without vocal cord movement (switch 14c: ON, switch 14d: OFF, switch 114a: OFF, switch 114b: ON)]
When a speaker performs an utterance without accompanying vocal cord movement, the in-body transmission sound accompanying the utterance is collected by the in-body conduction sound microphone 12 and converted into an analog electric signal. The collected body-transmitted sound is converted into a digital electric signal by the A / D converter 14f and input to the synchronization unit 14g. Under the control of the control unit 14b, the synchronization unit 14g adds control data such as data indicating the type of the signal to the digital electrical signal of the in-vivo transmission sound, and sends the data to the transmission unit 14j. The transmitter 14j transmits the transmitted digital electrical signal to the computer 120 via the connection line 30 as digital data or as network data based on a specific protocol. The collected analog electrical signal of the in-vivo transmission sound may be transmitted to the computer 120 as it is. In this case, the computer 120 performs conversion from an analog electric signal to a digital electric signal.

  The computer 120 generates voice data using the voice association model for the transmitted body conduction sound data, and generates bone conduction sound data using the bone conduction sound correspondence model. Is transmitted as a digital electrical signal to the sound reproduction device 110 (the operation of the computer 120 will be described later). The digital electric signal of the voice and the bone conduction sound transmitted from the computer 120 is received by the receiving unit 14k of the voice reproduction device 110. The audio digital electric signal is input to the D / A converter 14h via the switch 14c. The D / A converter 14 h converts the input digital audio signal into an analog signal and inputs the analog signal to the speaker 11. The digital electrical signal of bone conduction sound is input to the D / A converter 114d via the switch 114b. The D / A converter 114 d converts the input digital electric signal of the bone conduction sound into an analog signal and inputs the analog signal to the bone conduction vibrator 115. The speaker 11 reproduces the sound based on the input analog signal of the sound at a volume set by the user or set in advance, and the bone conduction vibrator 115 is based on the input analog signal of the bone conduction sound. Play the bones that sounded. In the case where the bone conduction sound analog signal is transmitted from the computer 120, the D / A converter 114d is not necessary, and the bone conduction vibrator 115 performs the bone conduction from the transmitted bone conduction sound analog signal. Play sound.

<Operation of computer 120>
Next, the operation of the computer 120 will be exemplified. FIG. 7 is a flowchart for explaining the operation of the computer 120 of this embodiment. Hereinafter, the operation of the computer 120 of this embodiment will be exemplified according to this figure.
First, the electrical signal transmitted from the audio reproduction device 110 is received by the receiving unit 20a (step S21). The received electrical signal is converted into data suitable for processing by the computer 120 as necessary, and the converted data is buffered in the temporary memory 20k and sent to the determination unit 20b in units of frames, for example.
The determination unit 20b determines whether or not the received reception data includes audio data and bone conduction data (step S22). Here, when it is determined that the received data includes audio data or bone conduction sound data, the following steps S23 to S29 are executed, and when it is determined that the received data does not include audio data. The following steps S30 to S35 are executed. This control is performed by the control unit 20m.

[When it is determined that the received data includes audio data (S23 to S29)]
First, the first feature amount extraction unit 20c analyzes the in-vivo transmission sound data transferred from the determination unit 20b, and extracts the feature amount (Y _j ) of the in-body transmission sound (step S23). Further, the second feature amount extraction unit 20d analyzes the voice data transferred from the determination unit 20b (the voice data synchronized in time with the in-body transmission sound data in step S23), and the voice feature amount (X _j ) is extracted (step S24). Further, the third feature quantity extraction unit 120d analyzes the bone conduction sound data transferred from the determination unit 20b (the bone conduction sound data synchronized in time with the in-body transmission sound data in step S23), and the bone conduction A sound feature quantity (Z _j ) is extracted (step S24). Note that the feature amounts extracted here are the same as those exemplified in the first embodiment.

Next, the in-vivo transmission sound feature value (Y _j ) extracted by the first feature value extraction unit 20c and the sound feature value (X _j ) extracted by the second feature value extraction unit 20d are, for example, The data is stored in the storage unit 120e in association with each other as a predetermined time interval (step S26). Further, the feature quantity (Y _j ) of the in-body transmitted sound extracted by the first feature quantity extraction unit 20c and the feature quantity (Z _j ) of the bone conduction sound extracted by the third feature quantity extraction unit 120d are, for example, Then, they are stored in the storage unit 120e in association with each other in units of frames that are predetermined time intervals (step S27).

Next, the speech learning unit 20f reads from the storage unit 20e the feature values (Y _j ) and the sound feature values (X _j ) of the in-vivo transmission sound that are associated with each other, and learning data that corresponds to these features. Then, by the learning process, the parameter of the voice association model indicating the correspondence between the feature quantity of any in-body transmitted sound and the feature quantity of any voice is calculated (step S28). Further, the bone conduction sound learning unit 120f reads in-vivo transmission sound feature values (Y _j ) and bone conduction sound feature values (Z _j ) associated with each other from the storage unit 120e, and these correspond to each other. As a learning data to be obtained, a bone conduction sound association model parameter indicating a correspondence relationship between a feature quantity of an arbitrary in-vivo transmitted sound and a feature quantity of an arbitrary bone conduction sound is calculated by learning processing (step S29). Note that the bone conduction sound association model is, for example, a mixed normal distribution model (GMM), as in Non-Patent Document 1, and the learning method is as described in the first embodiment, for example. As described in the first embodiment, the learning of the bone-conducted sound association model of the present embodiment does not require the association of both feature amounts by dynamic programming, and therefore the calculation time in the computer 120 is not necessary. As well as the effect that the variance of the model increases due to an error in association, and as a result the quality deterioration of the reproduced bone conduction sound does not occur. Note that steps S28 and S29 are not necessarily executed every time, and may be executed each time a predetermined number of feature amounts X _j and Y _j and feature amounts Z _j and Y _j are collected.

[When it is determined that the received data does not include audio data (S30 to S35)]
First, the first feature quantity extraction unit 20c analyzes the in-vivo transmission sound data transferred from the determination unit 20b, and extracts the feature quantity (Y _j ′) of the in-body transmission sound (step S30). The extracted feature quantity (Y _j ′) of the in-vivo transmitted sound is transferred to the speech association model application unit 20h and the bone conduction sound association model application unit 120h.

Similar to the first embodiment, the speech association model application unit 20h uses the parameters of the speech association model read from the storage unit 20g and the feature value (Y _j ′) of the in-vivo transmission sound, and transmits the in-vivo transmission for each j. calculating feature quantity of the sound 'feature of the corresponding audio _{(X j (Y j)'} a) (step S31). The calculated speech feature quantity (X _j ′) is transferred to the speech restoration unit 20i, and the speech restoration unit 20i synthesizes a speech waveform from the speech feature quantity (X _j ′) to generate speech data (step S32). ).

Further, the bone conduction sound association model application unit 120h uses the parameters of the bone conduction sound association model read from the storage unit 20g and the feature value (Y _j ′) of the body conduction sound, and uses the body conduction sound for each j. The feature amount (Z _j ′) of the bone conduction sound corresponding to the feature amount (Y _j ′) is calculated (step S33). The calculated bone conduction sound feature amount (Z _j ′) is transferred to the bone conduction sound restoration unit 120i, and the bone conduction sound restoration unit 120i synthesizes the bone conduction sound waveform from the bone conduction sound feature amount (Z _j ′). Then, bone conduction sound data is generated (step S34).
The voice data and the bone conduction data generated by the voice restoration unit 20i are transmitted from the transmission unit 20j to the voice reproduction device 110, and are reproduced by the speaker 11 and the bone conduction vibrator 115 of the voice reproduction device 110 as described above.

[Modifications, etc.]
The present invention is not limited to the embodiment described above. For example, in the first and second embodiments, the switch 14c may be turned OFF when speaking without accompanying vocal cord movement. As a result, the system can be used even in a quiet environment where leakage of feedback sound becomes a problem. Particularly in the second embodiment, since it is possible to perform feedback with bone conduction sound without performing voice feedback, a configuration in which the switch 14c can be turned off at the time of speech without accompanying vocal cord movement is effective.

In addition, the various processes described above are not only executed in time series according to the description, but may be executed in parallel or individually according to the processing capability of the apparatus that executes the processes or as necessary. Needless to say, other modifications are possible without departing from the spirit of the present invention.
Further, when the above-described configuration is realized by a computer, processing contents of functions that each device should have are described by a program. The processing functions are realized on the computer by executing the program on the computer.

  The program describing the processing contents can be recorded on a computer-readable recording medium. The computer-readable recording medium may be any medium such as a magnetic recording device, an optical disk, a magneto-optical recording medium, or a semiconductor memory. Specifically, for example, the magnetic recording device may be a hard disk device or a flexible Discs, magnetic tapes, etc. as optical disks, DVD (Digital Versatile Disc), DVD-RAM (Random Access Memory), CD-ROM (Compact Disc Read Only Memory), CD-R (Recordable) / RW (ReWritable), etc. As the magneto-optical recording medium, MO (Magneto-Optical disc) or the like can be used, and as the semiconductor memory, EEP-ROM (Electronically Erasable and Programmable-Read Only Memory) or the like can be used.

  The program is distributed by selling, transferring, or lending a portable recording medium such as a DVD or CD-ROM in which the program is recorded. Furthermore, the program may be distributed by storing the program in a storage device of the server computer and transferring the program from the server computer to another computer via a network.

  A computer that executes such a program first stores, for example, a program recorded on a portable recording medium or a program transferred from a server computer in its own storage device. When executing the process, the computer reads a program stored in its own recording medium and executes a process according to the read program. As another execution form of the program, the computer may directly read the program from a portable recording medium and execute processing according to the program, and the program is transferred from the server computer to the computer. Each time, the processing according to the received program may be executed sequentially. Also, the program is not transferred from the server computer to the computer, and the above-described processing is executed by a so-called ASP (Application Service Provider) type service that realizes the processing function only by the execution instruction and result acquisition. It is good. Note that the program in this embodiment includes information that is used for processing by an electronic computer and that conforms to the program (data that is not a direct command to the computer but has a property that defines the processing of the computer).

  As an industrial application field of the present invention, for example, a silent telephone using a non-audible tweet (NAM) can be exemplified.

FIG. 1A is a conceptual diagram illustrating the configuration of the entire system of the first embodiment. FIG. 1B is a conceptual diagram illustrating the configuration of the main body of the first embodiment. FIG. 2 is a block diagram illustrating a functional configuration of the computer according to the first embodiment. FIG. 3 is a flowchart for explaining the operation of the computer according to the first embodiment. FIG. 4 is a conceptual diagram illustrating the configuration of the entire system of the second embodiment. FIG. 5 is a conceptual diagram illustrating the configuration of the main body of the second embodiment. FIG. 6 is a block diagram illustrating a functional configuration of a computer according to the second embodiment. FIG. 7 is a flowchart for explaining the operation of the computer of the second embodiment.

Explanation of symbols

10,100 Voice reproduction device 20,120 Computer

Claims (4)

In a sound reproduction device that reproduces sound from internal body sound,
An in-body transmission sound collection unit that collects in-body transmission sounds resulting from utterances;
An audio collection unit that collects audio,
Synchronize temporally the in-vivo transmitted sound data collected by the in-vivo transmitted sound collection unit and the voice data collected by the voice collection unit at the same time when speaking with vocal cord movement. Is transmitted to a computer for learning a voice correspondence model that correlates the body-transmitted sound and voice, and the body-transmitted sound data collected by the body-transmitted sound collecting unit during speech without accompanying vocal cord movement A first transmitter for transmitting to the computer;
First reception in which the computer receives voice data generated by using the voice association model with respect to in-vivo transmitted sound data collected by the in-vivo transmitted sound collecting unit during utterance not accompanied by vocal cord movement And
An audio reproduction unit for reproducing audio from the audio data received by the first reception unit;
A bone conduction vibration part that collects bone conduction sound due to speech and reproduces bone conduction sound, and
The first transmitter is
Furthermore, in-situ transmission sound data collected by the in-vivo sound collection unit during speech with vocal cord movement and simultaneously with the bone conduction sound data collected by the bone-conduction vibration unit in time. To synchronize and send these to the computer to learn the bone conduction sound correspondence model that correlates the body conduction sound and the bone conduction sound,
The first receiver is
The computer receives bone conduction sound data generated by the computer using the bone conduction sound correspondence model with respect to the body conduction sound data collected by the body sound collection section during speech without the vocal cord movement. And
The bone conduction vibration part is
Reproducing bone conduction sound from the bone conduction sound data received by the first receiving unit;
An audio reproduction device characterized by that. The sound reproduction device according to claim 1,
An utterance state input unit that accepts an input operation indicating whether the utterance is accompanied by vocal cord movement or the utterance not accompanied by vocal cord movement;
When the input operation to the utterance state input unit indicates an utterance accompanied by vocal cord movement, the first transmission unit is configured to transmit in-vivo transmission sound data collected by the in-vivo transmission sound collection unit, At the same time, the bone conduction sound data collected by the bone conduction vibration unit is temporally synchronized and transmitted to the computer, and the bone conduction vibration unit does not reproduce the bone conduction sound.
When the input operation to the utterance state input unit indicates an utterance that does not involve vocal cord movement, the first transmission unit receives only the data of the in-vivo transmission sound collected by the in-body transmission sound collection unit. Transmitting to the computer, the first receiving unit receives the bone conduction sound data, the bone conduction vibration unit reproduces the bone conduction sound from the bone conduction data,
An audio reproduction device characterized by that. The sound reproduction device according to claim 1 or 2,
The above calculator
A second receiver for receiving data transmitted from the sound reproduction device;
A first feature amount extraction unit for extracting a feature amount of in-vivo transmitted sound using in-body transmitted sound data;
A third feature quantity extraction unit for extracting the feature quantity of the bone conduction sound using the bone conduction sound data;
The feature values of the internal transmission sound and the bone conduction sound that are synchronized in time are used as learning data that correspond to each other. A bone conduction sound learning unit for calculating a parameter of a bone conduction sound correspondence model indicating a correspondence relationship of
A bone-conducted sound association model applying unit that calculates the bone-conducted sound feature amount corresponding to the body-conducted sound feature amount using the parameters calculated by the bone-conducted sound learning unit and the body-conducted sound feature amount; ,
Using the bone conduction sound feature amount calculated by the bone conduction sound association model application unit, a bone conduction sound restoration unit that generates bone conduction sound data;
A second transmission unit for transmitting the bone conduction sound data generated by the bone conduction sound restoration unit to the sound reproduction device;
When the second receiving unit receives the temporally synchronized body-transmitted sound data and bone conduction sound data, the first feature amount extracting unit receives the body-transmitted sound data received by the second receiving unit. The third feature amount extraction unit extracts the bone conduction sound feature amount using the bone conduction sound data received by the second reception unit, and extracts the bone conduction sound feature amount. The sound conduction learning unit calculates the parameters of the bone conduction sound association model using these feature amounts,
When the second receiving unit receives only in-vivo transmission sound data, the first feature amount extraction unit extracts the in-body transmission sound feature amount using the in-vivo transmission sound data received by the second receiving unit. The bone conduction sound matching model application unit uses the extracted feature value of the body conduction sound and the parameter calculated by the bone conduction sound learning unit, and uses the extracted feature value of the body conduction sound to correspond to the feature value of the body conduction sound. The bone conduction sound restoration unit generates bone conduction sound data using the calculated bone conduction sound feature amount, and the second transmission unit calculates the bone conduction sound restoration unit. Send the bone conduction sound data generated in step 1 to the sound reproduction device.
An audio reproduction device characterized by that. In the sound reproduction method to reproduce the sound from the internal transmission sound,
A step in which a body-transmitted sound collecting unit collects body-transmitted sound resulting from utterance;
A step of collecting a sound by a sound collecting unit;
A step of extracting a feature amount of the in-vivo transmitted sound using a data of the in-vivo transmitted sound collected by the in-vivo transmitted sound collecting portion at the time of utterance accompanied by vocal cord movement;
A step of extracting a feature amount of the voice by using the voice data collected by the voice collecting unit at the time of utterance accompanied by a vocal cord movement;
The speech learning unit sets the feature values of the in-vivo transmission sound and the feature amount of the speech corresponding to the same sound collection time as learning data corresponding to each other, and the learning process performs the feature amount of the arbitrary in-vivo transmission sound and the Calculating a parameter of a voice association model indicating a correspondence relationship with a feature amount;
The first feature quantity extraction unit extracts the feature quantity of the in-vivo transmission sound using the data of the in-body transmission sound collected by the in-body transmission sound collection section at the time of utterance not accompanied by vocal cord movement;
The voice association model application unit uses the parameters calculated by the voice learning unit and the feature amount of the internal transmission sound collected by the internal transmission sound collection unit at the time of utterance not accompanied by vocal cord movement. Calculating a voice feature amount corresponding to the sound feature amount;
A step of generating a voice data using a voice feature amount calculated by the voice correlation model applying unit;
An audio reproduction unit reproducing audio from the audio data;
A step of collecting a bone conduction sound caused by an utterance by the bone conduction vibration unit;
A step of extracting a feature quantity of the bone conduction sound using a data of the bone conduction sound collected by the bone conduction vibration section at the time of utterance accompanied by vocal cord movement;
The bone conduction sound learning unit uses the feature values of the body conduction sound and the bone conduction sound corresponding to the same sound collection time as learning data corresponding to each other. Calculating a bone conduction sound correspondence model parameter indicating a correspondence relationship with a feature quantity of an arbitrary bone conduction sound;
The bone conduction sound association model application unit calculates the parameters calculated by the bone conduction sound learning unit and the feature amount of the internal body transmission sound collected by the internal body sound collection unit during speech without accompanying vocal cord movement. Extracting a bone conduction sound feature quantity corresponding to the body conduction sound feature quantity;
A step of generating bone conduction sound data by using the bone conduction sound feature amount calculated by the voice association model application unit, the bone conduction sound restoration unit;
The bone conductive vibrating portion includes the steps of reproducing the bone-conducted sound from the data of the bone-conduction sound,
An audio reproduction method characterized by comprising:

Images