JPH0632009B2 - Voice recognizer - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION The present invention will be described in the following order.

A Industrial field of use B Outline of invention C Conventional technology D Problems to be solved by the invention E Means for solving problems F Action G Example G1 Description of acoustic analysis circuit (Fig. 1) G2 time Description of Normalization Process (FIGS. 1, 2, and 3) Description of G3 Pattern Matching Process (FIG. 1) H Effect of Invention A Field of Industrial Application This invention is produced and stored in advance. The present invention relates to a voice recognition device which performs pattern matching between a standard pattern of a recognition target word and an input pattern of a word to be recognized.

B. Summary of the Invention In the present invention, in a device for performing voice recognition by pattern matching, as a pattern to be matched, a trajectory of an acoustic pattern time series obtained by acoustic analysis in a voice section of an input voice signal drawn in its parameter space is estimated. A new recognition parameter obtained by re-sampling the trajectory at a predetermined interval is used.The resampling interval for obtaining this recognition parameter is changed according to the estimated trajectory length, and the number of sample points is changed according to the trajectory length. In this case, the recognition accuracy is improved by obtaining sufficient information according to the change of the locus.

C Conventional technology Speech is a phenomenon that changes along the time axis,
A unique word or words are created by uttering a voice so that the pattern changes every moment. Speech recognition is the technology that automatically recognizes words and words spoken by humans.
At present, it is extremely difficult to realize voice recognition that is comparable to human auditory function. Therefore, most of the speech recognition currently in practical use is a method of performing pattern matching between a standard pattern of a recognition target word and an input pattern under a certain use condition.

FIG. 4 is a diagram for explaining the outline of this voice recognition device, in which the voice input from the microphone (1) is the acoustic analysis circuit (2).
Is supplied to. In this acoustic analysis circuit (2), acoustic parameters representing the characteristics of the input voice pattern are extracted. Various acoustic analysis methods for extracting the acoustic parameters are conceivable. For example, a bandpass filter and a rectifier circuit are provided as one channel, and a plurality of such channels are arranged with different passbands. There is known a method of extracting the time change of the spectrum pattern as the output of the. In this case, the acoustic parameters are the time series Pi (n) (i = 1, 2 ... I; I is the number of channels of the bandpass filter, n = 1, 2 ... N; N).
Can be represented by the number of frames used for recognition in the section determined by the voice section determination.

Acoustic parameter time series Pi (n) from this acoustic analysis circuit (2)
Is supplied to a mode switching circuit (3) including a switch, for example. When the switch of this circuit (3) is switched to the terminal A side, in the registration mode, the acoustic parameter time series Pi
(n) is stored in the standard pattern memory (4) as a recognition parameter. That is, the voice pattern of the speaker is stored in this memory (4) as a standard pattern prior to voice recognition. At the time of registration, generally, the number of frames of each registered standard pattern is different due to the variation in vocalization speed and the difference in word length.

On the other hand, when the switch (3) is switched to the terminal B side, it is in the recognition mode. Then, in this recognition mode, the acoustic parameter time series of the input voice at that time from the acoustic analysis circuit (2) is supplied to the input voice pattern memory (5) and temporarily stored. Then, the magnitude of the difference between this input pattern and each of the standard patterns of the plurality of recognition target words read from the standard pattern memory (4) is calculated by the distance calculation circuit (6). The recognition target word having the smallest difference from the pattern is detected by the minimum value judgment circuit (7), and the input word is recognized by this.

As described above, there are some that recognize the input voice by pattern matching processing of the registered standard pattern and the input pattern, but in this case, even if the same word is uttered in the same way, the spectrum pattern shifts in the time axis direction. It must be taken into consideration that it expands and contracts. That is, for example, when recognizing the word "high", when the standard pattern is registered as "high", and the input voice extends "hi" in the time axis direction, this is a big difference in distance. It is a completely different word, and I cannot recognize it correctly. Therefore, in pattern matching for voice recognition, it is necessary to perform time normalization processing for correcting the displacement and expansion / contraction in the time axis direction, and this time normalization is an important processing for improving recognition accuracy. is there.

As one method of this time normalization, DP (Dynamic Program
ng) There is a method called matching (for example, Japanese Patent Laid-Open No. Sho 5).
0-96104).

This DP matching method can be explained as follows.

The input pattern A is expressed as follows.

A = a ₁ a ₂ ... a _k ... a _k (1) Here, a _k is a quantity representing the feature of the voice at time k and is called a feature vector, and a _k = (a _k1 , a _k2 ,. _kq ... a _kQ )
It is represented by (2). Q is the order of the vector, and is equivalent to the number of channels when a bandpass filter group is used for acoustic analysis.

Similarly, the standard pattern of a specific word is B, which is expressed as follows.

B = b ₁ b ₂ ... b _l ... b _L (3) b _l = (b _l1 , b _l2 , ... b _lq , ... b _lq )
(4) The time normalization of the voice pattern can be regarded as performing a mapping operation between the time axes k and l of the input pattern A and the standard pattern B as shown in FIG.

This mapping is expressed as a function l = 1 (k) (5) and called a distortion function. If this distortion function is known, the time axis of the standard pattern B can be converted by this and aligned with the time axis k of the input pattern A. In other words, this distortion function converts the pattern B into the pattern B'aligned with the time axis k of the input pattern A.

Where B '= a _{1 (1)} b _{1 (2)} ... b _{1 (k)} ... b _{1 (K)}
(6)

Although this distortion function is unknown, it can be obtained from the optimum condition of this distortion function. That is, if one pattern, for example, a standard pattern is artificially distorted so that it is most similar to the other pattern (input pattern) (the distance is minimized), the original distortion disappears and the optimum distortion function is obtained. , Mapping pattern B ′ is obtained.

DP matching is a method for executing this principle, and obtains a mapping pattern B ′ by applying the following constraint to the distortion function.

(i) l _(k) is approximately a monotonically increasing function (ii) l _(k) is approximately a continuous function (iii) l _(k) takes a value near k.

What is required as a result of the matching process is the distance between the standard pattern and the input pattern, It is represented by. Where ‖ ‖ indicates the distance between two vectors. The minimum distance is the quantity D (A, B) representing the difference between the standard pattern B and the input pattern A after optimally normalizing the time and removing the time distortion. Can be defined by

Therefore, when there are a plurality of registered standard patterns, the amount D (A, B) of each standard pattern and the input pattern
Is determined, and it is determined that the amount D (A, B) matches the standard pattern with the minimum amount.

As described above, the DP matching does not prepare a large number of standard patterns in consideration of the shift of the time axis, but generates a standard pattern in which a large number of times are normalized by the distortion function, and the standard pattern is generated. Voice recognition is performed by obtaining the distance and detecting the minimum value.

By the way, when the above DP matching method is used, the number of frames of the registered standard patterns is indefinite, and the DP of all registered standard patterns and input patterns is
It is necessary to perform matching processing, and there is a drawback that the amount of calculation increases dramatically when the vocabulary increases.

Further, since the DP matching is a matching method that emphasizes the stationary part (the part where the spectrum pattern does not change with time), there is a possibility that erroneous recognition may occur between the partially similar patterns.

The present applicant has previously proposed a method of time normalization that does not cause such a defect (for example, Japanese Patent Application No. 59-106177).
issue).

That is, the acoustic parameter time series Pi (n) draws a sequence of points when considering its parameter space. For example, when the recognition target word is “HAI”, the number of acoustic analysis bandpass filters is two, and if Pi (n) = (P ₁ P ₂ ), the acoustic parameter time series of the input speech is the two-dimensional parameter. A series of points as shown in Fig. 6 is drawn in the space.
As is clear from this figure, the point sequence of the non-stationary part of the voice is roughly distributed, and the quasi-stationary part is densely distributed. This will be clear from the fact that if the speech is completely stationary, the parameters do not change, and in that case the point sequence will stop at one point in the parameter space.

From the above, it is considered that most of the deviation in the time axis direction due to the fluctuation of the vocalization rate of the voice is due to the difference in the point sequence density of the quasi-stationary part, and the influence of the time length of the non-stationary part is small. Therefore, if a locus drawn by a continuous curve that approximately passes through the entire point sequence as shown in FIG. 7 is estimated from the point sequence of this input parameter time series Pi (n), this locus will produce a vocal utterance. It can be seen that it is almost invariant to speed fluctuations.

Therefore, the applicant has proposed the following time axis normalization method. That is, first, the input parameter time series Pi
The trajectory drawn from the starting end Pi (1) to the ending end Pi (n) of (n) with a continuous curve Pi (s) is estimated, and the length S of the trajectory is obtained from the estimated curve Pi (s). Then, as shown in FIG. 8, re-sampling is performed at a predetermined length T along this locus. For example, when re-sampling to M points, the trajectory is re-sampled based on the length of T = S / (M-1) (9). Let Qi be the parameter time series that draws this resampled sequence of points.
(m) (i = 1, 2 ... I, m = 1, 2 ... M), this parameter time series Qi (m) has the basic information of the locus, and moreover, the speech production rate of the voice. It is a parameter that is almost invariant to fluctuations. That is, it is a recognition parameter time series whose time axis is normalized.

Therefore, this parameter time series Qi (m) is registered as a standard pattern, and the input pattern is also obtained as this parameter time series Qi (m).
If the distance between both patterns is calculated by i (m) and the one with the smallest distance is detected for voice recognition, the voice recognition is performed with the time-axis shift being normalized and removed. Is always done.

Then, according to this processing method, the recognition parameter time series Qi (m) is irrespective of the variation in the vocalization speed and the difference in the word length at the time of registration.
Since the number of frames of is always M, and the recognition parameter time series Qi (m) is time-normalized, the distance between the input pattern and the registered standard pattern can be calculated by the simplest Chebyshev distance calculation. You can expect a great effect.

Further, the above method is a method of time normalization in which the non-stationary part of the voice is more emphasized, and erroneous recognition between partially similar patterns such as DP matching processing is reduced.

Furthermore, the variation information of the speaking rate is the normalized parameter time series.
Since it is not included in Qi (m), it is easy to handle the global characteristics of the parameter transition structure coordinated in the parameter space, and it is possible to apply various effective methods for unspecified speaker recognition. .

Note that, in the following, this time normalization processing NAT (Normalizati
on Along Trajectory) process.

D Problems to be Solved by the Invention In the NAT processing described above, the recognition parameter Qi
When forming (m), in order to keep the number of frames constant at M, re-sampling is performed at intervals of a value T obtained by dividing the estimated trajectory length S of the trajectory by the number of frames M.

However, when the number of frames is constant and the number of resampling points is constant irrespective of the trajectory length, as shown in FIG. 9, for a simple trajectory in the case of a single syllable such as "A", For example, "Hokkaido" as shown in FIG.
Considering a complicated locus with a large number of syllables like this, if the number of frames is small, the parameters representing the locus of a single syllable as shown in FIG. 9 can be extracted, but the locus of a multi-syllable as shown in FIG. The number of frames, that is, the number of samples, is too small as a parameter for indicating, and is not sufficient as a parameter for indicating the characteristics of the trajectory. Conversely, if the number of frames is large, it is good for multi-syllables, but for single syllables,
This is a disadvantage that the number of frames is unnecessarily increased.

E Means for Solving Problems The present invention relates to a voice section determination circuit (24) for determining a voice section of an input voice signal, and an acoustic parameter within the voice section determined by the voice section determination unit (24). A feature extracting means (23) for obtaining a time series, an arithmetic means (81) for estimating the trajectory drawn by the acoustic parameter time series from the feature extracting circuit (23) in the parameter space, and obtaining this trajectory, and the calculating means Processing means (82) (8) for obtaining a recognition parameter time series by re-sampling at sample intervals according to the trace length
3), a standard pattern memory (4) in which the recognition parameter time series of the standard pattern of the recognition target word is stored, and the recognition parameter time series of the input pattern from the processing means (82) (83) and the standard pattern A distance calculation circuit (6) that calculates the difference between the recognition parameter time series of the standard pattern from the memory (4) and this distance calculation means (6) detects the smallest calculated value and outputs it. And a minimum value determination circuit (7) for obtaining

In the F action NAT process, the resampling interval is changed according to the trajectory length. Therefore, the number of resamples changes depending on the trajectory length, and the number of samples corresponds to a simple trajectory and a complex trajectory, and information is not insufficient for reproducing the trajectory.

G. Embodiment FIG. 1 shows an embodiment of a speech recognition apparatus according to the present invention, in which a bandpass filter group of 15 channels is used for acoustic analysis.

G1 Description of Acoustic Analysis Circuit (2) That is, in the acoustic analysis circuit (2), a microphone is used.
The audio signal from (1) is supplied to the A / D converter (213) via the amplifier (211) and the low pass filter (212) for band limitation, and for example, 1 at a sampling frequency of 12.5 kHz.
It is converted into a 2-bit digital audio signal. This digital audio signal is a digital bandpass filter for each channel of the 15-channel bandpass filter bank (22).
_{(221 0), (221 1} ), ‥‥, it is supplied to the (221 _14). This digital bandpass filter (221 ₀ ), (221 ₁ ), ..., (221
₁₄ ) is composed of, for example, a Butterworth fourth-order digital filter, and each band obtained by dividing the band from 250 Hz to 5.5 Hz at equal intervals on the logarithmic axis becomes the pass band of each filter. Each digital bandpass filter (221 _0), (221 _1), ‥‥, respectively rectifier circuit output signal of (221 ₁₄₎ (222 _0), (222 _1), ‥‥, supplied to (222 ₁₄₎ These rectification circuits (222 ₀ ), (222 ₁ ), ... (222 ₁₄ )
Are supplied to these rectifier circuits (222 ₀ ), (222 ₁ ) ,.
₁₄ ) outputs are digital low-pass filters (22
3 ₀ ), (223 ₁ ), ..., (223 ₁₄ ). These digital low-pass filters (223 ₀ ), (223 ₁ ), ..., (223 ₁₄ ).
Is constituted by, for example, an FIR low pass filter having a cutoff frequency of 52.8 Hz.

The output signal of each digital low-pass filter (223 ₀ ), (223 ₁ ), ... (223 ₁₄ ), which is the output of the acoustic analysis circuit (2), is supplied to the sampler (231) that constitutes the feature extraction circuit (23). It
This sampler (231) has a digital low pass filter (22
_{3 0), (223 1)} , ‥‥, sampled every frame period 5.12msec an output signal (223 _14). Therefore, the sample time series Ai (n) (i = 1, 2, ... 15;
n is a frame number and n = 1, 2, ..., N) is obtained.

The output from this sampler (231), that is, the sample time series Ai (n) is supplied to the sound source information normalization circuit (232), which eliminates the difference in vocal cord sound source characteristics depending on the speaker of the voice to be recognized. It In this way, the difference in the sound source characteristic is normalized and removed, and the acoustic parameter time series Pi (n) is obtained from the sound source information normalizing circuit (232). Then, the parameter time series Pi (n) is supplied to the intra-voice section parameter memory (233). The parameter memory (233) in the voice section receives the voice section determination signal from the voice section determination circuit (24), determines the parameter Pi (n) with the normalized sound source characteristic, and performs the staring for each voice section.

The voice section determination circuit (24) comprises a zero cross counter (241), a power calculation circuit (242) and a voice section determination circuit (243), and a digital voice signal from the A / D converter (213) is a zero cross counter (241). And the power calculation circuit (242). The zero-cross counter (241) counts the number of zero-crosses of the digital audio signal of 64 samples within this one-frame cycle every 5.12 msec,
The count value is supplied to the first input terminal of the voice section determination circuit (243). The power calculation circuit (242) obtains the power of the digital audio signal within one frame period, that is, the sum of squares, for each frame period, and the output power signal is the second input terminal of the audio section determination circuit (243). Is supplied to. The sound source normalizing information from the sound source information normalizing circuit (232) is further supplied to the third input terminal of the voice section determining circuit (243). And this voice section determination circuit (243)
In (1), the number of zero crosses, the power in the section, and the sound source normalization information are processed in a complex manner, and the process of determining silence, unvoiced sound, and voiced sound is performed to determine the speech section.

The voice section determination signal indicating the determined voice section from the voice section determination circuit (243) is supplied to the intra-voice section parameter memory (233) as the output of the voice section determination circuit (24).

In this way, the acoustic parameter time series Pi (n) stored in the memory (233) in the judgment voice section is extracted and supplied to the first NAT processing circuit (8).

Explanation of G2 time normalization processing This first NAT processing circuit (8) comprises a trajectory length calculation circuit (81), an interpolation interval calculation circuit (82) and an interpolation point extraction circuit (83).

The parameter time series Pi (n) read from the memory (223) is supplied to the trajectory length calculation circuit (81). This trajectory length calculation circuit
In (81), the length of the locus by linear approximation drawn by the acoustic parameter time series Pi (n) in the parameter space as shown in FIG. 2, that is, the locus length is calculated.

In this case, the Euclidean distance D (a _i , b _i ) between the one-dimensional vectors a _i and b _i is Is. Therefore, one-dimensional acoustic parameter time series Pi (n)
Therefore, the distance S (n) between adjacent parameters in the time series direction when the trajectory is estimated by linear approximation is S (n) = DPi (n + 1), Pi (n)) (n = 1, ..., N)
It is expressed as (11). Then, from the first parameter Pi (1) to the nth parameter time series Pi (n) in the time series direction.
Distance to SL (n) is Is represented. Note that SL (1) = 0.

And the total track length SL ₁ is Is represented. The trajectory length calculation circuit (81) is the equation (11), (12)
The signal processing shown in equation (13) is performed.

A signal indicating the locus length SL ₁ obtained by the locus length calculation circuit (81) is supplied to the interpolation interval calculation circuit (82). The interpolation interval calculation circuit (82) calculates the resampling interval T ₁ for resampling along the locus.

In this case, the sampling interval T ₁ is the trajectory length calculation circuit.
It can be changed according to the trajectory length d calculated in (81).
Then, in this example, the sampling interval T ₁ is determined as follows.

That is, the re-sampling number p is first determined for the trajectory length d. Then, the respective values d and p are changed in conjunction with each other. The optimum value of p for this value d is determined by experiments. For example, when the word length is about 0.5 seconds, d = 100, d = 30, d = 200, p = 45, and d = 50, p = 20.

Then, the sampling interval T ₁ is calculated as T ₁ = SL ₁ / (p−1) (14).

Sampling interval T ₁ from this interpolation interval calculation circuit (82)
Is supplied to the interpolation point extraction circuit (83), and the acoustic parameter time series Pi (n) from the memory (233) is also supplied to the interpolation point extraction circuit (83). In this interpolation point extraction circuit (83), the acoustic parameter time series Pi (n) is re-sampled at a sampling interval T ₁ as indicated by a circle in FIG. Sampling is performed, and a new acoustic parameter time series Ri (p) is formed from the obtained point sequence.

As described above, this parameter time series Ri (p) is one in which the number of frames p is varied according to the trajectory length, and can sufficiently represent the characteristics of the trajectory, and also normalization in the time axis direction is possible. Almost done.

This acoustic parameter time series Ri (p) is stored in the standard pattern memory.
Of course, it may be registered in (4) and used for pattern matching. In that case, this time series Ri (p)
May be subjected to DP matching processing.

However, if the DP matching process is used, the effect of the NAT process is halved. Therefore, in this example, the new acoustic parameter time series Ri (p) is supplied to the second NAT processing circuit (9) so that the characteristics of the NAT processing can be utilized.

That is, the second NAT processing circuit (9) is the trajectory length calculation circuit.
(91), interpolation interval calculation circuit (92) and interpolation point extraction circuit (93), the acoustic parameter time series Ri (p) is calculated by the trajectory length calculation circuit (9
Supplied to 1). In the trajectory length calculation circuit (91) as well as in the circuit (81), the trajectory length SL ₂ is calculated by linear approximation drawn by the acoustic parameter time series Ri (p) in the parameter space.

A signal indicating the trajectory length SL ₂ obtained by the trajectory length calculation circuit (91) is supplied to the interpolation interval calculation circuit (92), and the resampling interval T ₂ is calculated. In this case, this second NA
In the T processing, the number of frames is constant regardless of the word length, that is, the trajectory length. For example, if resampling is performed at M points, the resampling interval T ₂ is calculated as T ₂ = SL ₂ / (M−1) (15). To be

Resampling interval T from this interpolation interval calculation circuit (92)
_The signal indicating ₂ is supplied to the interpolation point extraction circuit (93). Also, the acoustic parameter time series Ri from the interpolation point extraction circuit (83)
(p) is also supplied to the interpolation point extraction circuit (93).
This interpolation point extraction circuit (93) is an acoustic parameter time series Ri (p).
Of the re-sampling interval T ₂ along the trajectory of the
Then, the recognition parameter time series Qi (m) is formed from the new point sequence obtained by this sampling.

Here, in the interpolation point extraction circuits (83) and (93), the processing according to the flowchart shown in FIG. 3 is performed to form the parameter time series Ri (p) and Qi (m), respectively.

In FIG. 3, the case of forming a new acoustic parameter time series Ri (p) from the acoustic parameter time series Pi (n) will be described, but the case of obtaining the recognition parameter time series Qi (m) from Ri (p) is also completely absent. The same is done.

First, in step [101], a value 1 is set to the variable J indicating the number of the resampling points in the time series direction, and the variable I indicating the frame number of the acoustic parameter time series Pi (n) is set.
A value of 1 is set in C and it is initialized. Next, in step [102], the variable J is incremented, and the step
By determining whether the variable J at that time is (P-1) or less in [103], the number in the time series direction of the re-sampling point at that time is the last number that needs to be re-sampled. Judge if not. If it is the last number, the process proceeds to step [104], and the re-sampling ends.

If it is not the last number, the resampling distance DL from the first resampling point to the Jth resampling point is calculated in step [105]. Next step [10
6] and the variable IC is incremented. Next step
In [107], the resample distance DL is the acoustic parameter time series Pi
Depending on whether it is smaller than the distance SL _[IC] from the first parameter Pi (1) of (n) to the IC-th parameter Pi _[IC] , the resampling point at that time is It is judged whether or not it is located on the starting point side of the locus with respect to the parameter Pi _{[IC], and} if it is not on the starting point side, the process returns to step [106] and the variable IC is incremented and then the re-sampling point is again set in step [107] And parameters Pi _[IC]
When the resampling point is judged to be located closer to the starting point side than the parameter Pi _[IC] on the trajectory by comparing the positions on the trajectory with and, the recognition parameter Pi _(J) is formed in step [108]. It

That is, from the resampling distance DL by the Jth resampling point, the distance SL _[IC by the (IC-1) th parameter Pi _[IC-1] located closer to the start point than the Jth resampling point is. _-1] is subtracted and the (IC-1) th parameter Pi
Find the distance SS from _[IC-1] to the Jth resampling point. Next, the distance S (n) between the parameter Pi _[IC-1] and the parameter Pi _[IC] located on both sides of this J-th resampling point on the locus (this distance S (n) is (11) This distance SS is divided by the signal processing shown in the formula), and this division result SS / S _[IC-1]
Parameters Pi located on either side of the th resampling point
Multiply the difference between _[IC] and Pi _[IC-1] to find the Jth
The (IC-1) th parameter Pi located adjacent to the start point side of this resampling point of the th resampling point
The interpolation amount from _[IC-1] is calculated, and this interpolation amount and the first (I) located adjacent to the start point side with respect to the J-th resampling point are calculated.
The (C-1) th parameter Pi _[IC-1] 'is added to form a new acoustic parameter Ri _(J) along the trajectory.

In this way, the start and end points (these are Ri (1) = Pi (o) =
O, Ri (p) = Pi _(s) . The recognition parameter character string Ri (p) is formed by resampling the (P-2) points excluding).

When estimating the trajectory and resampling, if you always start from silence, the voice section determination circuit (2
Even if there is a deviation in the judgment section in 4), there is almost no influence on the trajectory and resampling. In this case, the end point of the locus and the end point of resampling may be the silent part.

G3 Explanation of pattern matching processing The recognition parameter time series Qi (m) from the second NAT processing circuit (9) is controlled by the mode changeover switch (3), and in the registration mode, the standard pattern memory (4) for each recognition target word. Will be stored in. In the recognition mode, the distance calculation circuit
It is supplied to (6) and the distance from the standard pattern memory (4) to the parameter time series of the standard pattern is calculated. The distance in this case is calculated as a simple Chebyshev distance, for example. The calculation output of the distance between each standard pattern and the input pattern from this distance calculation circuit (6) is the minimum value judgment circuit.
The standard pattern that is supplied to (7) and has the smallest distance calculation value is determined, and the result of this determination provides the recognition result of the input voice at the output end (70).

H Effect of the Invention In the present invention, since the sampling interval of resampling is changed according to the trajectory length of the trajectory drawn by the acoustic parameter time series in the NAT processing, the length of the word length,
That is, it is possible to prevent the recognition rate from deteriorating due to a slight difference in the number of syllables.

[Brief description of drawings]

FIG. 1 is a block diagram of an embodiment of the device of the present invention, FIG. 2 is a diagram for explaining the same, FIG. 3 is a diagram for explaining an operation of a main part thereof, and FIG. FIG. 5 is a block diagram showing the basic configuration of the recognition device, FIG. 5 is a diagram for explaining DP matching, FIGS. 6 to 8 are diagrams for explaining NAT processing, and FIGS. It is a figure which shows the example of the locus which a parameter time series draws in the case of a syllable and a multi-syllable. (2) is an acoustic analysis circuit, (4) is a standard pattern memory, (6) is a distance calculation circuit between the standard pattern and the input pattern, (7) is a minimum value determination circuit, and (8) is a first NAT processing circuit. , (9) are the second NAT processing circuits.

 ─────────────────────────────────────────────────── ─── Continuation of the front page (72) Makoto Akabane Makoto Akabane 6-735 Kita-Shinagawa, Shinagawa-ku, Tokyo, Sony Corporation (56) References Acoustics Society of Japan, October 59, 1959 -9P. 17-18

Claims (1)

[Claims] 1. (a) a voice section determining means for determining a voice section of an input voice signal; (b) a feature extracting means for obtaining an acoustic parameter time series within the voice section determined by the voice section determining means. , (C) Estimating the trajectory of the acoustic parameter time series drawn by the feature extracting means in the parameter space, and calculating the trajectory, and (d) Re-sampling at sample intervals according to the trajectory length obtained by the calculating means. A processing means for obtaining a recognition parameter time series by sampling, (e) a standard pattern memory in which the recognition parameter time series of the standard pattern of the recognition target word is stored, and (f) an input pattern of the input means from the above processing means. A distance calculation means for calculating the difference between the recognition parameter time series and the recognition parameter time series of the standard pattern from the standard pattern memory, and (g) the value calculated by this distance calculation means. A voice recognition device comprising a minimum value determining means for detecting the smallest one and obtaining a recognition output.