TW201705019A - Text-to-speech method and multi-lingual speech synthesizer using the method - Google Patents

Abstract

A text-to-speech method and a multi-lingual speech synthesizer using the method are disclosed. The multi-lingual speech synthesizer and the method executed by a processor are applied for processing a multi-lingual text message in a mixture of a first language and a second language into a multi-lingual voice message. The multi-lingual speech synthesizer comprises a storage device configured to store a first language model database, a second language model database, a broadcasting device configured to broadcast the multi-lingual voice message, and a processor, connected to the storage device and the broadcasting device, configured to execute the method disclosed herein.

Description

Text-to-speech method and multi-language speech synthesis Device

The present disclosure relates to a text-to-speech method, and more particularly to a text-to-speech method and a synthesizing device for processing multi-language text messages into multi-lingual voice information.

With the development of the global market, multilingual expressions are often used in everyday language or articles. In particular, when referring to proper nouns, foreign names, foreign place names, and foreign unique vocabulary in the professional field, it will not be possible to express them with translated nouns.

The general text-to-speech (TTS) method can only process a single language, and find corresponding voice messages in the database of the language according to the text content, thereby synthesizing the pronunciation corresponding to the specific text content. Audio. However, when another language exists in the text message, the traditional text-to-speech method will be difficult to handle because the matching voice message cannot be found in the database.

The present invention provides a text-to-speech method, which is executed by a processor for converting a multi-language text message having a first language and a second language into a multi-language voice message, and matching one with a plurality of a first language model database of a phonological phoneme label and a first language linguistically connected tone information, and a second language model data having a plurality of second language phoneme tags and a second language linguistically connected tone information The text-to-speech method includes: dividing the multi-language text message into at least one first language paragraph and at least one second language paragraph; converting the first language paragraph to at least one first language phoneme label and converting at least one The second language paragraph is at least one second language phoneme tag; using the at least one first language phoneme tag to search the first language model database to obtain at least one first language phoneme tag string, and utilizing the at least one The second language phoneme tag searches the second language model database to obtain at least one second language phoneme tag string; a textual order of the multilingual text message, combining the at least one first language phoneme tag string and the at least one second language phoneme tag string into a multilingual phoneme tag string; in each of the two adjacent A cross-language connection tone data is generated at a boundary of the phoneme tag string, wherein each two adjacent phoneme tag strings includes a first language phoneme tag string in the at least one first language phoneme tag string And a second language phoneme tag string of the at least one second language phoneme tag string; combining the multi-lingual phoneme tag string, each of the at least one first language phoneme tag string a first language contemporaneous connection tone material at a junction between two adjacent phoneme tags, each of the at least one second language phoneme tag string The second language of the second language is connected to the tone data at a junction between adjacent phoneme tags and the cross language connection tone data to generate the multilingual voice audio; and the multilingual voice audio is output.

The present invention further provides a multi-language speech synthesis device for processing multi-language text messages having a first language and a second language into multi-language speech messages, the synthesizing device comprising: a storage module storing a plurality of first languages a first language model database in which the phoneme label and the first language are connected to the same language, and a second language model database having a plurality of second language phoneme tags and a second language language connected tone information; a group, playing the multi-language voice audio; the processor is coupled to the storage module and the broadcast module to distinguish the multi-language text message into at least one first language paragraph and at least one second language paragraph; converting the first The language segment is at least one first language phoneme tag and converts the at least one second language segment into at least one second language phoneme tag; using the at least one first language phoneme tag to find the first language model database to obtain at least a first language phoneme tag string, and using the at least one second language phoneme tag to find a second language model Retrieving a library to obtain at least one second language phoneme tag string; combining the at least one first language phoneme tag string with the at least one second language phoneme tag string in accordance with a textual order of the multilingual text message a multi-language phoneme tag string; generating a cross-language connection tone data at the intersection of each two adjacent phoneme tag strings, wherein each two adjacent phoneme tag strings include the at least one a first language phoneme tag string in a language phoneme tag string and a second language phoneme tag string in the at least one second language phoneme tag string; combining the multi-language phonetic symbols a string, a homonym connected tone material of the first language at a boundary between each two adjacent phoneme tags in the at least one first language phoneme tag string, at the at least one a second language of the same language connection tone data at a boundary between each two adjacent phoneme tags in the two-language phoneme tag string and the cross-language connection tone data to generate the multi-lingual voice information; And outputting the multi-language voice audio to the broadcast module.

The above and other objects, features, advantages and embodiments of the present disclosure will become more apparent and understood.

100‧‧‧Text-to-speech system

120‧‧‧ storage module

140‧‧‧Broadcast module

160‧‧‧ processor

180‧‧‧ Radio Module

200‧‧‧Text-to-speech method

LMD1~LMD2‧‧‧Language Model Database

PU1~PU3‧‧‧ pronunciation unit

AU1a~AU3c‧‧‧ candidate audio information

L1, L2‧‧‧ connection path

Pavg1, Pavg2‧‧‧ benchmark average frequency

PAU, PAU1~PAU820‧‧‧ tone frequency data

PCAND‧‧‧ candidate frequency data

ML‧‧‧ training voice

SAM, SAM1, SAM2‧‧‧ sample recording

LAN1, LAN2‧‧‧ language

P1, P2‧‧ ́s tone

T1, T2‧‧‧ rhythm

F1, F2‧‧‧ tone

S210~S270, S241~S244, S246~S247‧‧‧ steps

S251~S252, S310~S330‧‧‧ steps

The above and other objects, features, advantages and embodiments of the present disclosure will be more apparent and understood. The description of the drawings is as follows: FIG. 1 is a multi-language speech synthesis apparatus according to an embodiment of the present disclosure. FIG. 2 is a flow chart of a text-to-speech method according to an embodiment of the present invention; FIG. 3 and FIG. 4 are a flow chart of step S204 according to an embodiment of the present invention; FIG. 6A to FIG. 6B are diagrams illustrating a method for calculating candidate audio information according to an embodiment of the present disclosure; FIG. 7 is a diagram illustrating a method for calculating candidate audio information according to an embodiment of the present disclosure; FIG. 8 is a flowchart showing a training method of a training program according to a text-to-speech method according to an embodiment of the present invention; and FIG. 9A to FIG. 9C Show mixed language according to an embodiment of the present case Schematic diagram of the training speech ML, the sampled speech SAM, and the analyzed intonation, rhythm, and timbre of the mixed language.

The embodiments are described in detail below with reference to the accompanying drawings, but the embodiments are not intended to limit the scope of the present disclosure, and the description of structural operation is not intended to limit the order of execution, any component recombined. The structure, which produces equal devices, is covered by this case. In addition, the drawings are for illustrative purposes only and are not drawn to the original dimensions.

The terms “first”, “second”, etc. used in this document are not specifically intended to refer to the order or order, nor are they used to limit the case, but merely to distinguish the elements described in the same technical terms or Just do it. Secondly, the words "including", "including", "having," "containing," etc., as used herein are all terms of an open term, meaning, but not limited to.

Please refer to FIG. 1, which is a functional block diagram of a multi-language speech synthesis apparatus in accordance with an embodiment of the present disclosure. As shown in FIG. 1 , a multi-language speech synthesis device 100 includes a storage module 120, a broadcast module 140, and a processor 160.

The multi-lingual speech synthesis device 100 is configured to process/convert text messages into corresponding multi-language speech audio, and the broadcast module 140 is configured to output the multi-language speech audio. In one embodiment, the multi-lingual speech synthesis apparatus 100 can process text messages that include multiple languages simultaneously.

In one embodiment, the storage module 120 is configured to store a majority Each language model database, such as LMD1 and LMD2, has a plurality of language model databases corresponding to a single language (for example, Chinese, English, Japanese, German, French, Spanish, etc.). Each language model database contains a plurality of phoneme labels in a single language and the same language connection tone information between the phoneme tags. In one embodiment, the storage module 120 stores a set of Chinese language model database LMD1 and another set of English language model database LMD2 as an illustrative description. However, in this paper, the type of language is not limited to this. In one embodiment, a mixed multilingual model database for both Chinese and English languages is not required.

The phoneme label is the smallest unit of sound that can distinguish between pronunciations. In one embodiment, a word or word may consist of one to several syllables, and a syllable may consist of one to several phonemes. In one embodiment, in Chinese, for example, each character contains only one syllable, and this syllable usually consists of one to three phonemes (each phoneme resembles a phonetic symbol). In one embodiment, for English, each English word contains at least one syllable, and each syllable contains one to more phonemes (each phoneme resembles an English phonetic symbol). In one embodiment, in order to achieve a proper pronunciation effect, each language model database stores stored tone information in addition to the pronunciation of each phoneme itself. Among them, the connected tone information is a tone (Tone) used to connect (or between a word and a word) before and after a continuous sounding between adjacent phonemes.

The phoneme label is a representative symbol that is convenient for the system to process. In fact, the language model database LMD1~LMD2 is further stored. There are audio information such as pitch, tempo, and timbre required for each phoneme label to be synthesized. In one embodiment, for example, the tone includes, but is not limited to, the frequency of the utterance; the rhythm includes, but is not limited to, the speed, spacing, and rhythm of the utterance; the timbre includes, but is not limited to, the quality of the vocalization, the shape of the mouth, the location of the utterance, and the like.

Referring to FIG. 2, a flow chart of a text-to-speech method according to an embodiment of the present invention is shown. The multi-language text-to-speech method 200 is used to process/convert text messages containing different languages in different languages into multi-language voice audio. In one embodiment, the processor 160 is configured to perform a multi-language text-to-speech method. The processor 160 can be, for example, but not limited to, a central processing unit (CPU), a system on chip (System on Chip, SoC), application processor, audio processor, digital signal processor or a specific function processing chip or controller.

In one embodiment, the multi-language text message can be, but is not limited to, a paragraph in the document, an instruction entered by the user, a circled text on the web page, or text entered by various other sources. In one embodiment, the first language model database has a plurality of first language phoneme tags and the same language connection tone information in the first language, the second language model database has a plurality of second language phoneme tags and a second The same language of the language is connected to the tone information.

As shown in FIG. 2, the multi-language text-to-speech method 200 includes the following steps. In step S210, the multi-language text message is divided into at least one first language paragraph and at least one second language paragraph. In one embodiment, the multi-language text message is differentiated by the processor 160 according to different languages. For most language passages. In one embodiment, the text message "put a Jason Mraz to listen" will be divided into three language paragraphs, namely "put a" (Chinese language paragraph), "to listen" (Chinese language paragraph) and "Jason Mraz" (English language paragraph).

In step S220, the at least one first language paragraph is converted into at least one first language phoneme label, and the at least one second language paragraph is converted into at least one second language phoneme label. In one embodiment, each phoneme tag may include, but is not limited to, audio information such as pitch, tempo, timbre, etc. of the phoneme.

In step S230, the first language model database is searched by using at least one first language phoneme tag to obtain at least one first language phoneme tag string, and the second language model database is searched by using at least one second language phoneme tag. Obtaining at least one second language phoneme tag string.

In one embodiment, M represents a phoneme of Chinese (Mandarin), and the numbers represent the numbers of different phonemes in Chinese. In one embodiment, the Chinese character "put" corresponds to two phoneme labels [M04] and [M29], and the Chinese character "one" corresponds to the other two phoneme labels [M09] and [M25]. Therefore, the phoneme label string converted from the Chinese language paragraph "put one" is [M04 M29 M09 M25]; similarly, the language paragraph "to listen" can also be converted into another corresponding phoneme label. Tandem, for [M08 M29 M41 M44]. On the other hand, the English language paragraph "Jason Mraz" is converted into a corresponding set of other phoneme tag strings according to the English language model database LMD2, which is [E19 E13 E37 E01 E40].

In step S240, the text according to the multi-language text message In sequence, at least one first language phoneme tag string and at least one second language phoneme tag string are combined into a multi-lingual phoneme tag string.

In other words, the processor 160 arranges a plurality of phoneme tag sequences of different language segments according to the order of the original multi-language text messages, and combines the arranged phoneme tag strings into a multi-language phoneme tag string. . In this example, the text message "put a Jason Mraz to listen" is converted into three phonetic label strings, that is, [M04 M29 M09 M25], [E19 E13 E37 E01 E40], and [M04 M29 M09 M25] According to the order of the original multi-language text messages, they are combined into a multi-language phoneme tag string [M04 M29 M09 M25 E19 E13 E37 E01 E40 M08 M29 M41 M44].

In step S250, the processor 160 generates a cross-language connection tone data at the boundary of every two adjacent phoneme tag sequences, wherein each two adjacent phoneme tag strings includes at least one first language tone. A first language phoneme tag string in the bit tag string and a second language phoneme tag string in the at least one second language phoneme tag string. In one embodiment, processor 160 looks up language model repositories LMD1 and LMD2 to obtain cross-language connection tone data for every two adjacent phonetic tag strings. An embodiment will be described below.

In step S260, the processor 160 merges the multi-lingual phoneme tag string, the same language connection in the first language at the boundary between each two adjacent phoneme tags in the at least one first language phoneme tag string. Tone material, homolingual tonal data of a second language at the interface between every two adjacent phoneme tags in at least one second language phoneme tag string, and cross-language Connect tone data to produce multi-language voice audio. In step S270, multi-language voice audio is output.

In order to achieve a better sounding effect, in one embodiment, step S240 of the text-to-speech method of FIG. 2 further includes steps S241-S245 as shown in FIG.

As shown in FIG. 3, in step S241, the processor 160 divides the combined multi-lingual phoneme tag string into a plurality of first pronunciation units, each of the pronunciation units belonging to a single language and including at least one first language. And a continuous phoneme tag of the phoneme tag string and the corresponding one of the at least one second language phoneme tag string.

Next, step S242 is performed for each first pronunciation unit. In step S242, the processor 160 determines whether the number of candidates corresponding to the first pronunciation unit is greater than or equal to the language model database corresponding to the first pronunciation unit in the first language model database and the second language model database. a predetermined number corresponding to the first sounding unit. When the number of candidates of each of the first pronunciation units in the language model database corresponding to the first language model database and the second language model database is greater than or equal to a corresponding predetermined number, the processor 160 performs step S243. The join value of each candidate path is calculated, wherein each candidate path passes through one candidate for each first pronunciation unit. In step S244, the processor 160 determines a connection path between every two adjacent first sounding units according to the joining value of each candidate path.

In one embodiment, in step S244, the processor 160 further determines a previous first pronunciation in the two adjacent first pronunciation units. a connection path between one candidate selected in the meta and a selected one of the candidates in the next first pronunciation unit of the two adjacent first pronunciation units, wherein the first first pronunciation of the two adjacent first pronunciation units The candidate selected in the unit and the candidate selected in the latter first pronunciation unit of the two adjacent first pronunciation units are located in one of the candidate paths of the lowest added value.

However, after step 242, when the number of candidates of any one or more first pronunciation units in one of the corresponding language model databases of the first language model database and the second language model database is less than a corresponding predetermined number, execution is performed Sub-steps S246 and S247 in an embodiment of the invention as shown in Fig. 4 (shown as A in Fig. 3).

In step S246 in FIG. 4, the processor 160 further divides the one or more first sounding units into a plurality of second sounding units, wherein the length of any one of the second sounding units is smaller than the corresponding one of the first The length of the pronunciation unit. In step S247, for each second pronunciation unit, the processor 160 further determines a language module database corresponding to the first pronunciation unit in the first language model database and the second language model database, and the second pronunciation unit Whether the corresponding number of candidates is greater than or equal to a predetermined number corresponding to the second sounding unit.

In other words, in step S242, if the first language model database and the second language model database are in one of the corresponding language model databases, any one or more of the first pronunciation units (or the second pronunciation unit, etc.) When the number of candidates is determined to be less than the corresponding predetermined number, sub-steps S246 and S247 are repeated until the number of candidates is determined to be greater than or equal to the corresponding predetermined number, and then in step S243, the join cost of each candidate path is calculated. value.

In one embodiment, the multi-language text message "We will go to Boston University next week to attend the graduation ceremony" is divided into a number of first pronunciation units, such as audio information "we", "next week", "together", " Go, "Boston University", "Participate in the graduation ceremony." The processor 160 determines whether the number of candidates of the first pronunciation units in one of the first language model database and the second language model database is greater than or equal to a predetermined number corresponding to the first pronunciation unit.

In one embodiment, it is assumed that the predetermined number of candidates for the first pronunciation unit "participating in the graduation ceremony" is ten. If there are only five candidates for the first pronunciation unit "joining the graduation ceremony" in the first language model database LMD1, this means that The number of candidates in the first language model database LMD1 is less than the corresponding predetermined number. Then, the first pronunciation unit "participation graduation ceremony" is divided into second pronunciation units smaller than the length of the first pronunciation unit "participation graduation ceremony", as shown in step S246 in FIG.

In one embodiment, the predetermined number of each second sounding unit is the same as the predetermined number of corresponding first sounding units. In another embodiment, the predetermined number of each of the second sounding units is set to be different from the predetermined number of the corresponding first sounding units. In this embodiment, the first pronunciation unit "participating in the graduation ceremony" is divided into two second pronunciation units: "participation" and "graduation ceremony", and the words "participation" and "graduation ceremony" are respectively used to find the first language. The model database LMD1 obtained 280 and 56 candidate numbers. For example, in the present embodiment, the second pronunciation unit "participation" and "graduation ceremony" The predetermined number of candidates is ten. This means that the number of candidates corresponding to the second pronunciation unit "participation" and "graduation ceremony" is greater than the corresponding predetermined number. Next, step S243 is performed. In order to obtain a better pronunciation effect, the first sounding unit is further divided into second sounding units of shorter length until a sufficient number of candidates can be found in the corresponding language database.

As shown in FIG. 5, in one embodiment, step S250 of generating cross-language connection tone material at the interface between every two adjacent phoneme tag strings further includes sub-steps. The connection relationship between the phoneme labels of the pronunciation units of the same language is stored in the language model databases LMD1 and LMD2, respectively. As an example, the multi-language phoneme label string [M04 M29 M09 M25 E19 E13 E37 E01 E40 M08 M29 M41 M44] with the text message "Listen to Jason Mraz" will be used to connect the same language to the tone information [M04 M29] is stored in the Chinese model database LMD1, represented by L[M04, M29], the same language connection tone information of [M29 M09] is represented by L[M29, M09], and so on. The same language connection tone information of any two adjacent phoneme labels in Chinese is stored in the language model database LMD1. In one embodiment, the same language connection tone information [E19 E13] of the adjacent phoneme label is pre-stored in the English model database LMD2, and so on.

Since each language model database LMD1 and LMD2 respectively store data of the same language message, the multi-language phoneme label string cannot be found in the traditional text-to-speech method [M04 M29 M09 M25 E19 E13 E37 E01 E40 M08 M29 M41 M44 Cross-language connection tone data across two languages (eg [M25 E19] cross-language connection tone data] And [E40 M08] cross-language connection tone data).

The connection tone data between the phoneme labels makes the voice fluent, consistent and continuous. Thus, in one embodiment, in accordance with step S250, processor 160 generates cross-language connection tone material at the intersection of any two phoneme labels between two different languages, as will be described in detail below.

Figure 5 is a flow diagram of a method of generating cross-language connection tone material at the interface between the first language and the second language. In one embodiment, as shown in FIG. 5, step S250 further includes sub-steps S251-S252.

In step S251 of FIG. 5, the processor replaces the phoneme tag of the corresponding first language phoneme tag having the approximate pronunciation with the first phoneme tag of the at least one second language phoneme tag string in at least one second language. The first phoneme label of the phoneme label string.

In one embodiment, in the multi-language text message "put a Jason Mraz to listen", the first boundary between the first language and the second language is between "one" and "Jason". In this embodiment, Chinese is the first language, English is the second language, and the Chinese character "one" (corresponding to the phoneme label [M09 M25]) appears in the English text "Jason" (corresponding phoneme label [E19 E13]) front. That is, in the present embodiment, the first vowel bit label of the first language language paragraph and the first boundary of the first phoneme label of the second language language paragraph are at the phoneme label [M25] and [E19 ]between.

According to step S251, the first phoneme label [E19] in the second language (in this embodiment is English) language paragraph is replaced with the phoneme label of the first language (in this embodiment, Chinese) having an approximate pronunciation. . In a real In the example, the phoneme "Ja" (corresponding to the phoneme label [E19]) in English is replaced with the phoneme "ㄐ" in Chinese (pronounced "Ji", corresponding to the phoneme tag [M12]). In the present embodiment, the phoneme label [E19] of the phoneme "Ja" in English is replaced with the phoneme tag [M12] of the phoneme "ㄐ" in Chinese.

Further, in the same example text ("put a Jason Mraz to listen"), the second cross-language interface is "Mraz" (corresponding to the phoneme label [E37 E01 E40]) and "coming" (corresponding phoneme label) Junction between [M08 M29]). That is, the second junction is between the last vowel label of the second language language paragraph and the first phoneme label of the first language language paragraph. In the present embodiment, the second boundary exists between the phoneme labels [E40] and [M08]. Next, replace the phoneme label [M08] of the phoneme "来来" in Chinese with the phoneme tag [E21] of the phoneme "le" in English (the phoneme tag with the phoneme "来" in Chinese [ M08] approximate).

Next, in step S252, the processor 160 searches the first language model database LDM1 by using the corresponding phoneme label in the first language phoneme label to obtain at least one first language phoneme label in the first language model database LDM1. Corresponding homophone connection tone information between the last phoneme label of the serial port and the corresponding phoneme label in the first language phoneme tag, wherein the corresponding language connection tone information in the first language model database LDM1 Used as a cross-language at the junction between the first language phoneme label in the at least one first language phoneme tag string and the second language phoneme tag in the at least one second language phoneme tag string Connect tone data.

In the previous embodiment, for the first junction, the last phoneme label and the replaced phoneme label at the first boundary according to the first language Sign [M25 M12], find the same language connection tone information L[M25 M12] at the first boundary in the first language model database LMD1. Then, the same language connection tone information L[M25 M12] is regarded as the cross-language connection tone data at the first boundary. For the second junction, the last phoneme label at the second junction in the second language and the most approximate replacement phoneme label [E40 E21] can also find the same language in the second language model database LMD2. Connect tone information [E40 E21]. Next, the same language connection tone information L[E40 E21] is regarded as the cross-language connection tone data at the second boundary.

An embodiment is presented below through FIGS. 6A and 6B to illustrate how to calculate the number of candidates for the target audio information.

As shown in FIG. 6A, assuming that the currently selected pronunciation unit is "attending the graduation ceremony", the tone, rhythm, or timbre corresponding to each word of "joining the graduation ceremony" is found in the first language model database LMD1. The pitch contains the frequency of the utterance; the rhythm includes the duration, velocity, interval, and rhythm of the utterance; the timbre includes the quality of the vocalization, the shape of the mouth, and the location of the vocalization. In the examples of FIGS. 4B and 4C, the tone is used as a comparison reference.

In the present embodiment, the pitch and rhythm of the pronunciation unit (e.g., the length of the duration) can be represented by a one-dimensional Gaussian model, respectively. For example, the Gaussian model of one dimension of the pitch is the statistical distribution of the pronunciation unit at different frequencies (such as Hertz Hz); the Gaussian model of one dimension of the duration is the pronunciation unit at different time lengths ( The statistical distribution of units such as milliseconds ms).

In this embodiment, the mouth shape representing the tone can be used in multiple A Gaussian mixture model of dimensions is established. In one embodiment, Speaker Adaptation can be utilized to create such a multi-dimensional Gaussian blend model to record a mouth shape representing a timbre. Use Speaker Adaptation technology to create a more reliable mouth shape for incoming text messages. The implementation of the Speaker Adaptation technique involves: using a large number of corpora of the same language but different language to first establish a universal model of all phonemes in that language; a general model for establishing all phonemes in the language; Then, the sound segment corresponding to the language extracted from the originally recorded mixed language audio file is used to extract the lip shape parameter; then the general model of each previously existing phoneme is moved to the extracted lip shape parameter sample, and moved. The new model is the adapted model. The detailed steps and principles of the speaker adaptation technique are explained and explained in Reynolds, Douglas A., in the journal article "Speaker Verification Using Adapted Gaussian Mixture Models" published in 2000 by Digital Signal Processing. In fact, the speaker adaptation technique is only one of the implementation methods for establishing an oral model in this case, but the disclosure is not limited thereto.

In this example, first find the reference average frequency Pavg1 of the respective intonations of the "attending graduation ceremony" in the language model database LMD1. In this example, the average frequency of the six Chinese characters participating in the graduation ceremony is 100Hz, 140 Hz, 305 Hz, 203 Hz, 150 Hz, and 143 Hz. This set of reference average frequency Pavg1 is used as the target audio information, which is the criterion for subsequent selection.

Then find out all the matches in the language model database LMD1 The 168 sets of tone frequency data PAU of the pronunciation unit of the "Graduation Ceremony" are shown as PAU1~PAU168 as shown in FIG. 6A. In one embodiment, the amount of difference between the tone frequency data and the selected group of target audio frequencies (i.e., the reference average frequency Pavg1) is set to within a predetermined range, such as within 20% of a reference average frequency Pavg1. In this embodiment, the predetermined ranges of the target audio information of the six Chinese characters are: 100HZ±20%, 100Hz±20%, 146Hz±20%, 305Hz±20%, 230Hz±20%, 150Hz±20. % and 143 Hz ± 20%. The audio information of all six words in the set is within the predetermined range as a candidate (PCAND). For example, in the first set of tone frequency data PAU1, the frequencies of the six Chinese characters are 175 Hz, 179 Hz, 275 Hz, 300 Hz, 120 Hz, and 150 Hz, respectively, which are outside the predetermined range of 20% of the reference average frequency Pavg1. In this example, there are only two sets of candidate frequency data PCAND in the 168 group, which are the tone frequency data PAU63 and PAU103, respectively, and the difference is within a predetermined range. However, assuming that the predetermined number of first pronunciation units is 10, the number of candidates (i.e., 2 groups, PAU 63 and PAU 103) is smaller than the predetermined number (i.e., 10). Therefore, the first sounding unit needs to be divided into a plurality of second sounding units shorter than the length of the first sounding unit to obtain more candidates.

Next, the first pronunciation unit "Participation Graduation Ceremony" is divided into a plurality of second pronunciation units, "participation" and "graduation ceremony". One of the second pronunciation units "Graduation Ceremony" is taken as an example for further explanation. In Fig. 6B, in an embodiment, the respective tone average frequencies Pavg2 of the second pronunciation unit "Graduation Ceremony" in the first language model database LMD1 are found. In one embodiment, the average frequency of the second pronunciation unit "graduation ceremony" is The order is 305 Hz, 203 Hz, 150 Hz, and 143 Hz. This set of reference average frequency Pavg2 is used as the target audio information, which is the criterion for subsequent selection.

Then, all the tone frequency data PAU corresponding to the second pronunciation unit "graduation ceremony" in the first language model database LMD1 are found, and a total of 820 sets of tone frequency data PAU1~PAU820 are included. In one embodiment, in the first set of tone frequency data PAU1, the frequencies of the six Chinese characters are 275 Hz, 300 Hz, 120 Hz, and 150 Hz. Next, a group having a difference from the target audio frequency (that is, the reference average frequency Pavg) within a predetermined range (i.e., within 20% of the reference average frequency Pavg2) is selected from among the tone frequency data sets PAU1 to PAU820. In this example, the group of the tone frequency data difference amount within the predetermined range has 340 sets of candidate frequency data PCAND. At this time, the number of candidates of the target audio information is sufficient, so the length of the second sounding unit is appropriate. Therefore, it is no longer necessary to divide the second sounding unit into smaller-length sounding units. The predetermined range is not limited to 20%, and may be adjusted to other reasonable ranges above and below the reference average frequency.

In the embodiments of FIGS. 6A and 6B above, only the method of selecting candidate audio information by using the tone frequency data is schematically illustrated. In another embodiment, the candidate audio information is selected based on the weight of the pitch, tempo, and timbre.

For example, the target audio information AUavg is expressed as: AUaug=αPavg+βTavg+γFavg

Among them, Pavg is the average frequency of the tone, Tavg is the average length of the rhythm, and Favg is the average mouth shape of the tone. In one embodiment, the mouth shape can be represented by a multi-dimensional matrix. In one embodiment, the frequency of the frequency can be scrambled The Mel-frequency cepstral coefficient (MFCC) is used to represent the shape of the mouth, which is well known to those skilled in the art and is not the main scope of discussion in this case, and will not be further described herein. α, β, and γ are the weights of each of Pavg, Tavg, and Favg. α, β, γ are all greater than 0 and the three are added to 1. In one embodiment, the candidate audio information is selected by referring to the pitch, tempo, and timbre weighting results of the individual audio data of the target audio information AUavg and the language model database LMD1.

FIG. 7 is a schematic diagram showing an operation example of determining a connection path of a sounding unit according to an embodiment of the present invention.

As shown in FIG. 7, in one embodiment, the text message is finally divided into a pronunciation unit PU1 (for example, a Chinese character), a pronunciation unit PU2 (for example, an English phrase), and a pronunciation unit PU3 (for example, an English phrase). . In this embodiment, when the language model database LMD1~LMD2 is searched, four different candidate audio information AU1a~AU1d are found for the pronunciation unit PU1; two different candidate audio information AU2a~AU2b are found for the pronunciation unit PU2. Find three different candidate audio information AU3a~AU3c for the pronunciation unit PU3.

And, the connection path L1 between the candidate audio information AU1a~AU1d to the candidate audio information AU2a~AU2b and the connection tone between the candidate audio information AU2a~AU2b to the candidate audio information AU3a~AU3c are obtained by the language model database LMD1~LMD2 Information L2.

Each candidate path contains a fluency cost, and each connection path also contains a fluency cost. Step S254 is used to find a connection with the least fluency cost in the combination of the connection path L1 and the connection path L2. The path minimizes the fluency of the three pronunciation units PU1~PU3 and the two connection paths L1 and L2, so that the selected connection path has the highest overall fluency.

The calculation formula for calculating the minimum value of the fluency cost is as follows: among them, Representing all candidate audio information for each pronunciation unit, Representative and All candidate audio information of another adjacent sounding unit. The summed fluency cost is equal to the target value of each candidate audio information in each pronunciation unit (such as C _Target ( )), the spectral value of the candidate audio information connected between two adjacent pronunciation units (such as C _Spectrum ( , )), the tone value of the candidate audio information connected between two adjacent pronunciation units (such as C _Pitch ( , )), the rhythmic value of the candidate audio information connected between two adjacent pronunciation units (such as C _Duration ( , )) and the strength value of the candidate audio information connected between two adjacent pronunciation units (such as C _Intensity ( , ))) The weighted sum of the equations is obtained. In the formula, α, β, γ, δ, and ε represent the weights of the target generation value, the sound spectrum generation value, the tone generation value, the rhythm generation value, and the strength generation value, respectively. The fluency cost obtained by combining the weighted sums of the different paths of L1 and L2 is compared, and the path with the lowest weighted sum is used as the seed audio for the final synthesis.

Through the above weighting calculation, the aggregated fluency cost on each path can be obtained, and a path with the lowest overall fluency cost can be found. In one embodiment, the sum of the candidate audio information AU1c to the candidate audio information AU2b to the candidate audio information AU3a has the lowest fluency cost, and the candidate audio information AU1c, the candidate audio information AU2b, and the candidate audio information on the path are selected. AU3a is used as the seed audio for final synthesis in the text-to-speech method.

Next, step S260 in FIG. 2 is executed, and the processor 160 converts the audio information (such as the audio information AU1c, AU2b, and AU3a) of each pronunciation unit in series to generate multi-language voice information. The multi-language voice audio can be played through the broadcast module 140. As shown in step S270 in FIG. 2, the sound output in the text-to-speech method 200 of the present invention is achieved. In this embodiment, the sounding module 140 can be, but is not limited to, a speaker and/or a telephone handset.

Further, in the above embodiment, the language model databases LMD1 to LMD2 are pre-established through a training program. In one embodiment, the text-to-speech method 200 proposed in the present application includes, in addition to the above-described pronunciation program, a training program for how to establish/train the appropriate language model database LMD1~LMD2.

As shown in FIG. 1, the multi-lingual speech synthesis device 100 further includes a radio module 180. In the embodiment, the sound collection module 180 can be built in the text-to-speech system 100 or independently peripherally used in the multi-language speech synthesis device 100. In one embodiment, the radio module 180 can be, but is not limited to, Microphone or recording unit.

In one embodiment, the radio module 180 is configured to sample at least one training speech for the training program of the language model database LMD1~LMD2. The language model database LMD1~LMD2 generated by the training is provided to the text-to-speech system 100 for use.

FIG. 8 is a flow chart showing a training method for a training program according to an embodiment of the present disclosure. Referring to FIGS. 8 and 9A-9C, in the training program of the text-to-speech method 200 in FIG. 8, step S310 is first performed, and the training voice of at least one single language is received by the radio module 180. 9A to 9C are diagrams showing the mixed speech training speech ML, the sampled recording SAM, and the analyzed mixed language pitch, tempo, and timbre information. The pitch includes, but is not limited to, the frequency of the utterance; the rhythm includes, but is not limited to, the duration, velocity, interval, and rhythm of the utterance; the timbre includes, but is not limited to, the quality of the vocalization, the shape of the mouth (eg, MFCC) ) and the sounding parts, etc.

In one embodiment, as shown in FIG. 9A, the sampled recording SAM of the training speech ML is taken from a native Chinese speaker, and the Chinese-speaking person can smoothly use both Chinese and English. In this way, the mixed Chinese and English voices can be obtained, and the connection between Chinese and English is smooth. For the same reason, if you are recording in English as a native speaker, you need to be able to use both Chinese and English smoothly.

In another embodiment, the training voice includes a first sampled recording in pure Chinese and a second sampled recording in pure English, respectively native to Chinese. Individuals and native speakers of English are recorded separately. Next, step S320 is performed to analyze the pitch, tempo or timbre of the two different languages in the sample of the training speech. As shown in Fig. 9B, the training speech ML of the mixed language in Fig. 9A is first divided into the sample recording SAM1 of the first language LAN1 and the sample recording SAM2 of the second language LAN2. Next, as shown in FIG. 9C, the sampled recording SAM1 of the first language L1 and the sampled recording SAM2 of the second language L2 respectively analyze a pitch, a tempo, or a timbre to obtain, for example, a frequency and a sound length. Audio information such as mouth shape. Thereafter, the tone P1 of the sample recording SAM1, the rhythm T1 and the tone F1, and the tone P2 of the sample recording SAM2, the rhythm T2 and the tone F2 are obtained.

Among them, the tone P1 and the tone P2 are the frequency distributions of all the pronunciation units in the sample recording SAM1 and the sample recording SAM2, the horizontal axis is a different frequency band (the unit is Hertz Hz), and the vertical axis is the number of sampling points. The rhythm T1 and the rhythm T2 are the sound length distributions of all the pronunciation units in the sample recording SAM1 and the sample recording SAM2, respectively, the horizontal axis is a different time length (the unit is millisecond ms), and the vertical axis is the number of sampling points. A single sampling point is a single audio frame of each of the sampled recording SAM1 or sampled recording SAM2.

In this embodiment, the timbre F1 and the timbre F2 are the mouth shapes of all the utterance units in the sampled recording SAM1 and the sampled recording SAM2, respectively, as shown in FIG. 9C, and are respectively represented by a plurality of multi-dimensional Gaussian mixture models.

Sampling recordings of different voices L1 and L2 SAM1/sampling recording SAM2 respectively obtained tone P1, rhythm T1 and tone F1 and tone P2 The rhythm T2 and the timbre F2 will be stored in the corresponding language model databases LMD1~LMD2, respectively.

Next, step S330 is executed to store the training speech whose tones, rhythms, and timbres fall within a predetermined range. The tone, rhythm or timbre of the training speech is compared to a reference range. In one example, the reference range may be the middle range of sounds recorded in the past, for example, within a range of two standard deviations above or below the mean of the intonation, rhythm, or timbre. This step involves excluding portions of the sample of training speech that have tones, rhythms, or timbres that fall outside of the reference range. In this way, extreme tone, rhythm or tone can be excluded, or the content of the sample recording is obviously inconsistent (for example, Chinese native speakers and English-speaking people have too much difference in tone). To ensure consistency of intonation, rhythm, or tone between the two languages in the database.

That is, if one of the newly recorded training speech's intonation, rhythm, and timbre deviates significantly from the intermediate value of the statistical distribution model of the past recorded data (eg, the intonation, rhythm, and timbre fall twice the original statistical distribution model) In addition to the difference, or falling within a certain cumulative distribution, such as 10% to 90%, the newly recorded training speech is filtered to avoid excessively large tones, rhythms and timbres (for example, the vocalist suddenly adopts Sharp or more vocal pronunciation) affects the consistency of candidate audio information in the language model database. The training speech is stored in the language model database LMD1 or LMD2 in different languages.

As described in the above embodiments, multi-language text messages are converted into multi-language voice messages, thereby improving the fluency and consistency of speech. Continuity.

Although the present invention has been disclosed in the above embodiments, it is not intended to limit the present case. Anyone having ordinary knowledge in the field can make various changes and refinements without departing from the spirit and scope of the case. Therefore, the scope of protection of this case is This is subject to the definition of the scope of the patent application.

200‧‧‧Text-to-speech method

S210~S270‧‧‧Steps

Claims (16)

A text-to-speech method is implemented by a processor for converting a multi-language text message having a first language and a second language into a multi-language voice message, and matching a plurality of first language tones a first language model database in which the bit label and the first language are connected to the same language, and a second language model database having a plurality of second language phoneme labels and a second language connected tone information. The text-to-speech method includes: dividing the multi-language text message into at least one first language paragraph and at least one second language paragraph; converting the first language paragraph into at least one first language phoneme label and converting at least one second language The paragraph is at least one second language phoneme tag; the first language model database is searched using the at least one first language phoneme tag to obtain at least one first language phoneme tag string, and the at least one second language is utilized The phoneme tag looks up the second language model database to obtain at least one second language phoneme tag string; according to the multilingual a textual order of the text message, combining the at least one first language phoneme tag string and the at least one second language phoneme tag string into a multi-lingual phoneme tag string; in each two adjacent phonemes A cross-language connection tone data is generated at a boundary of the tag string, wherein each of the two adjacent phoneme tag strings includes a first language phoneme tag string in the at least one first language phoneme tag string And a second language phoneme tag string in the at least one second language phoneme tag string; Merging the multilingual phoneme tag string, the first language consonant connection tone data at a boundary between each two adjacent phoneme tags in the at least one first language phoneme tag string And a language-connected tone material of the second language at a boundary between each two adjacent phoneme tags in the at least one second language phoneme tag string and the cross-language connection tone data to generate The multi-lingual voice audio; and outputting the multi-language voice audio. The text-to-speech method of claim 1, wherein the first language phoneme tag string in the at least one first language phoneme tag string is listed in the at least one second language phoneme tag string The step of generating the cross-language connection tone data in front of the second language phoneme tag string includes: replacing the first phoneme tag of the at least one second language phoneme tag string with the at least one second language tone The first phoneme tag of the bit tag string has a corresponding phoneme tag of the corresponding first language phoneme tag; and the first phoneme tag of the first language phoneme tag is used to find the a language model database for obtaining the first language model between a last phoneme label of the at least one first language phoneme tag string and the corresponding phoneme tag of the corresponding first language phoneme tag Corresponding linguistic connection tone information in the database, wherein the corresponding linguistic connection tone information in the first language model database is used as the first language in the at least one first language phoneme tag string a boundary between the phoneme tag string and the second language phoneme tag string in the at least one second language phoneme tag string This cross-language connection tone material. The text-to-speech method of claim 1, wherein the first language model database and the second language model database each further comprise a phrase, a word formed by consecutive phoneme labels, Audio information of one or a combination of characters, syllables, or phonemes, and one of a phrase, word, word, syllable, or phoneme formed by the consecutive phoneme tags Or a combination thereof is a single pronunciation unit. The text-to-speech method according to claim 3, wherein the at least one first language phoneme tag string and the at least one second language phoneme tag string are combined into one according to a text order of the multi-language text message. After the step of serializing the multi-lingual phoneme label, the text-to-speech method further comprises the steps of: dividing the multi-language phoneme tag string into a plurality of first pronunciation units, each of the first pronunciation units being in a single language, And each of the first pronunciation units includes a continuous phoneme label of the at least one first language phoneme tag string and the corresponding one of the at least one second language phoneme tag string; a first pronunciation unit determining whether a candidate number corresponding to one of the first pronunciation units is greater than or equal to the first language model database and a corresponding language model database in the second language model database a predetermined number corresponding to a pronunciation unit; when the first language model database and the second language model database a candidate number of each of the first pronunciation units in a corresponding language model database is greater than or equal to the corresponding predetermined number, and a value of each of the candidate paths is calculated, wherein each of the candidate paths passes each of the One of the candidates of a pronunciation unit; and determining a connection path between each two adjacent first sounding units according to the joining value of each candidate path. The text-to-speech method of claim 4, wherein the determining the connection path between each two adjacent first sounding units comprises: determining a previous one of the two adjacent first sounding units a connection path between a candidate selected in a pronunciation unit and a selected one of the next first pronunciation units of the adjacent two first pronunciation units; wherein the previous one of the two adjacent first pronunciation units The candidate selected in the first pronunciation unit and the candidate selected in the latter first pronunciation unit of the two adjacent first pronunciation units are located in one of the candidate paths with the lowest added value. The text-to-speech method as claimed in claim 4, wherein the candidate number of any one or more first pronunciation units in the first language model database and the corresponding language model database of the second language model database When the predetermined number is less than the corresponding number, the text-to-speech method further comprises: dividing each of the one or more first sounding units into a plurality of second sounding units, wherein any one of the second sounding units The length is smaller than the length of the corresponding first pronunciation unit; for each of the second pronunciation units, one of the first language model database and the second language model database is determined, one of the corresponding language model databases Whether the number of candidates corresponding to the second pronunciation unit is greater than or equal to a predetermined number corresponding to the second pronunciation unit. The text-to-speech method of claim 4, wherein the added value of each candidate path is a target value of each candidate audio information in each first pronunciation unit, and each two adjacent first pronunciation units are connected The spectral value of the candidate audio information, the pitch value of the candidate audio information in each of the two adjacent first sounding units, the rhythmic value of the candidate audio information in each of the two adjacent first sounding units, And a weighted sum of strength values connecting the candidate audio information in each of the two adjacent first pronunciation units. The text-to-speech method of claim 1, wherein the first language model database and the second language model database are pre-established by a training program, wherein the training program comprises: receiving training speech in at least one single language And analyzing the intonation, rhythm, and timbre of the training speech; and storing the training speech, the rhythm, and the timbre being within a respective predetermined range. A multi-language speech synthesis device for processing multi-language text messages having a first language and a second language into multi-lingual speech sounds The synthesizing device includes: a storage module, a first language model database storing a plurality of first language phoneme tags and a first language connected tone information, and a plurality of second language phonemes tags and a second language model database for connecting the tone information in the same language; a broadcast module for playing the multi-language voice audio; and a processor coupled to the storage module and the broadcast module for: the multi-language text The message is divided into at least one first language paragraph and at least one second language paragraph; converting the first language paragraph into at least one first language phoneme label and converting the at least one second language paragraph into at least one second language phoneme label; Locating the first language model database with the at least one first language phoneme tag to obtain at least one first language phoneme tag string, and using the at least one second language phoneme tag to search the second language model database Obtaining at least one second language phoneme tag string; according to the text order of the multi-language text message, the at least one first The phoneme tag string and the at least one second language phoneme tag string are combined into a multi-lingual phoneme tag string; a cross-language connection tone is generated at the intersection of each two adjacent phoneme tag strings Data, wherein each two adjacent phoneme tag strings comprise a first language phoneme tag string in the at least one first language phoneme tag string and the at least one second language phoneme tag string a second language phoneme tag string; merging the multi-lingual phoneme tag string in the at least one first language a first language contemporaneous connection tone material at a junction between every two adjacent phoneme tags in the phoneme tag string, every two in the at least one second language phoneme tag string a second language of the same language at a junction between adjacent phoneme tags and the interlingual connection tone data to generate the multilingual voice audio; and outputting the multilingual voice audio to the broadcast Module. The multilingual speech synthesis apparatus of claim 9, wherein the first language phoneme tag string in the at least one first language phoneme tag string is listed in the at least one second language phoneme tag string When the second language phoneme label is in front of the string, the processor generates the cross-language connection tone data to: replace the first phoneme tag of the at least one second language phoneme tag string with the at least one second language The first phoneme tag of the phoneme tag string has a corresponding one of the first language phoneme tags of the approximated pronunciation; and the first phonetic tag of the first language phoneme tag is used to find the first phoneme tag a language model database for obtaining the first language model data between a last phoneme label of the at least one first language phoneme tag string and the corresponding phoneme tag of the corresponding first language phoneme tag a corresponding language connection tone information in the library, wherein the corresponding language connection tone information in the first language model database is used as the first language tone in the at least one first language phoneme tag string The cross-language connection tone material at a boundary between the bit tag string and the second language phoneme tag string in the at least one second language phoneme tag string. The multilingual speech synthesis device of claim 9, wherein the first language model database and the second language model database each further comprise a phrase, a word formed by consecutive phoneme labels. Audio information of one or a combination of characters, syllables, or phonemes, and the words, words, words, syllables, or phonemes formed by the consecutive phoneme tags One or a combination thereof is a single pronunciation unit. The multilingual speech synthesis apparatus of claim 11, wherein the at least one first language phoneme tag string is combined with the at least one second language phoneme tag string in accordance with a textual order of the multilingual text message After the step of stringing a multi-lingual phoneme label, the processor is further configured to: divide the multi-language phoneme tag string into a plurality of first pronunciation units, each of the first pronunciation units belong to a single language, and a continuous phoneme tag comprising the at least one first language phoneme tag string and the corresponding one of the at least one second language phoneme tag string; for each first pronunciation unit, determining In the corresponding language model database of the first language model database and the second language model database, whether the number of candidates corresponding to one of the first pronunciation units is greater than or equal to a predetermined one of the first pronunciation unit a number; each of the first pronunciation model in the first language model database and the corresponding language model database in the second language model database Greater than equal to the number of candidate corresponding to the predetermined number, calculating each of the candidate Selecting a value of the join value, wherein each of the candidate paths passes through one of each of the first pronunciation units; and determining, between each two adjacent first pronunciation units, based on the join value of each candidate path Connection path. The multi-lingual speech synthesis device of claim 12, when determining the connection path between each two adjacent first pronunciation units, the processor is further configured to: determine two adjacent first pronunciations a connection path between the selected one of the previous first pronunciation units in the unit and the selected one of the next one of the two first first pronunciation units; wherein the two adjacent first The candidate selected in the previous first pronunciation unit of the pronunciation unit and the candidate selected in the latter first pronunciation unit of the two adjacent first pronunciation units are located in the candidate path of the lowest value of the added value. The multilingual speech synthesis apparatus of claim 12, wherein the candidate of any one or more of the first pronunciation units in the first language model database and the corresponding language model database of the second language model database When the number is less than the corresponding predetermined number, the processor is further configured to: divide each of the one or more first sounding units into a plurality of second sounding units, wherein any one of the second sounding units The length is less than the length of the corresponding first pronunciation unit; for each of the second pronunciation units, determining the first language model data Whether the number of candidates corresponding to one of the second pronunciation units is greater than or equal to a predetermined number corresponding to the second pronunciation unit in the corresponding language model database of the library and the second language model database. The multilingual speech synthesis device of claim 12, wherein the join value of each candidate path is a target value of each candidate audio information in each first pronunciation unit, and each two adjacent first pronunciations are connected The spectral value of the candidate audio information in the unit, the pitch value of the candidate audio information in each of the two adjacent first sounding units, and the rhythmic value of the candidate audio information in each of the two adjacent first sounding units And a weighted sum of the strength values of the candidate audio information in each of the two adjacent first sounding units. The multilingual speech synthesis device of claim 9, wherein the first language model database and the second language model database are both pre-established by a training program, wherein the training program comprises: receiving training in at least one single language Speech; analyzing the intonation, rhythm, and timbre of the training speech; and storing the speech, the rhythm, and the timbre within the respective predetermined range of the training speech.