Speech signal processing apparatus for detecting a speech signal from a noisy speech signal

Abstract: There is disclosed a signal detection apparatus for detecting a noise-suppressed speech signal. In the signal detection apparatus, a band division process including a Fourier transformation is performed for an inputted speech signal, thereby outputting spectrum signals of plural channels. A cepstrum analysis process is performed for the spectrum signals, and a peak of the obtained cepstrum is detected in response to the cepstrum analysis result. Thereafter, a speech signal interval of the inputted noisy speech signal is detected in response to the detected peak, and a noise is predicted in the speech signal in response to the detected speech signal interval. Then, the predicted noise is canceled in the spectrum signals thereby outputting noise-suppressed spectrum signals. Finally, the noise-suppressed spectrum signals are combined and are inverse Fourier-transformed, thereby outputting a noise-suppressed speech signal.

Description: The present invention relates to a speech signal processing apparatus, and more particularly, to a signal detection apparatus for detecting a noise-suppressed speech signal from a noisy speech signal and detecting a kind of the detected speech signal, a signal processing apparatus for discriminating between speeches of plural speakers to be provided in the previous step of a speech recognition apparatus, and a speech recognition apparatus for recognizing a speech spoken by a speaker. Conventionally, there has been suggested an apparatus for discriminating between a speech and a noise in response to a noisy speech signal (See, for example, the Japanese patent laid open publication (JP-A) No. 59-124397/1984, and the Japanese patent laid open publication (JP-A) No. 59-131997/1984). The discrimination result outputted from the apparatus is used in an apparatus for recognizing a speech such as a speech recognition apparatus. However, the conventional apparatuses for discriminating between a noise and a speech can not detect a kind of the speech, namely, can not detect who speaks. In a general speech recognition apparatus, it is desired that a kind of the speech is previously detected. Further, conventionally, in a conventional speech recognition apparatus, a word spoken by a speaker is often recognized by inputting a speech signal of a particular speaker thereto. Namely, in order to heighten a recognition rate upon pattern-matching of speech features, there is considered that an information who now speaks is inputted to the speech recognition apparatus. Alternatively, in a speech recognition apparatus provided in a system requiring for keeping a secret, it is desired that only a particular speech is accepted. Namely, it is desired to judge who speaks and to use information of the judgment result at the previous step of the speech recognition apparatus. Further, recently, there has been developed a system which can be used only if a particular word is spoken in a place requiring for keeping a secret such as a laboratory. In this system, by utilizing a speech recognition technique, when a key word is spoken toward this system using key words such as "one", "three", "five", "seven", this system is enabled. In the system of this type requiring for keeping a secret, it is necessary not only to use the above-mentioned key word but also to limit the speaker to a particular one. Namely, it is desired that the system is constituted so that the system is enabled only when a predetermined particular one speaks a key word toward the system in order to further keep a secret. The object of the present invention is therefore to provide a signal detection apparatus capable of detecting a noise-suppressed speech signal from a noisy speech signal. Another object of the present invention is to provide a signal detection apparatus capable of detecting a speech and a kind of the speech in response to a speech signal. A further object of the present invention is to provide a signal processing apparatus capable of discriminating between speeches of plural speakers and outputting respective discriminated speech signals. A still further object of the present invention is to provide a speech recognition apparatus capable of recognizing only a speech of a predetermined particular speaker and outputting a speech signal thereof. In accordance with the invention, a speech recognition apparatus comprises: first storage means for peak frequencies of plural speakers which are previously obtained by performing a band division process including a Fourier transformation for speech signals of plural speakers and performing a cepstrum analysis process for the transformed speech signals; band division means for performing a band division process including a Fourier transformation for an inputted noisy speech signal and outputting spectrum signals of plural channels; cepstrum analysis means for performing a cepstrum analysis process for the spectrum signals of plural channels outputted from said band division means and outputting a cepstrum analysis result; peak frequency detection means for detecting a peak frequency in response to the cepstrum analysis result outputted from said cepstrum analysis means; first comparison means for detecting which speaker the inputted noisy speech signal corresponds to by comparing the peak frequency detected by said peak frequency detection means with the peak frequencies stored in said first storage means, and outputting a first comparison result; second storage means for storing predetermined features of speeches of plural speakers; speech cutting-out means for cutting out a speech from the inputted noisy speech signal; feature extraction means for extracting a feature of the speech cut out by said speech cutting-out means; second comparison means for recognizing a speech by comparing the feature of the speech extracted by said feature extraction means with the features of the speeches stored in the second storage means outputting a second comparison result; and comparison combining means for performing a predetermined process for the first comparison result outputted from said first comparison means and the second comparison result outputted from said second comparison means. A preferable embodiment is defined in dependent claim 2. Fig. 1 is a schematic block diagram showing a first signal detection apparatus useful for describing the present invention; Fig. 2 is a schematic block diagram showing a second signal detection apparatus useful for describing the present invention; Fig. 3 is a schematic block diagram showing a noise suppression apparatus useful for describing the present invention; Figs. 4a and 4b are graphs showing a cepstrum analysis used in the present invention; Fig. 5 is a graph for showing a noise prediction method used in the present invention; Figs. 6 and 7 are diagrams showing waveforms for explaining a noise cancellation method used in the preferred embodiments of the invention; Fig. 8 is a schematic block diagram showing a speech recognition apparatus of a first preferred embodiment according to the present invention; and Fig. 19 is a schematic block diagram showing a speech recognition apparatus of a second preferred embodiment according to the present invention. Referring to Fig. 1, the signal detection apparatus comprises a cepstrum analysis section 1, a storage unit 2, a signal judgment section 3, a maximum likelihood estimator 4 and a signal output section 5. The cepstrum analysis section 1 performs a cepstrum analysis for an inputted speech signal which is previously Fourier-transformed by a band division section. A cepstrum is obtained by calculating a logarithm of an amplitude spectrum for a short time interval of a waveform of the inputted speech signal and inverse-Fourier-transforming the calculated logarithm. Fig. 4a shows an amplitude spectrum for a short time interval of a speech signal, and Fig. 4b shows a cepstrum of the speech signal. The storage unit 2 is provided for previously storing predetermined feature information of known kinds of plural speech signals, and is constituted by a RAM or ROM. Namely, for example, the storage unit 2 stores information of a relationship between a cepstrum analysis result of a speech signal of a speaker A and the speaker A. The cepstrum analysis result includes at least a presence position of a peak or a pitch of the obtained cepstrum and formant information, as shown in Fig. 4b. Similarly, the cepstrum analysis result of speeches of the other speakers B, C and so on is previously stored in the storage unit 2. The presence positions of peaks or pitches and the formant information of respective speakers are different from each other every speaker. It is to be noted that, as feature information of a speech, there may be used any speech feature such as an amplitude and a phase of a speech and so on in addition to the above-mentioned cepstrum analysis result. The signal judgment section 3 discriminates between a speech and a noise of a noisy speech signal by utilizing the cepstrum obtained by the cepstrum analysis section 1. As a method for detecting a speech signal interval by utilizing the cepstrum of the speak signal, there has been known to those skilled in the art, for example, a method for discriminating between a speech signal and a noise by detecting a peak or a pitch of the cepstrum. Namely, as shown in Fig. 2, the signal judgment section 3 may comprise a peak detection section 31 for detecting a peak of the obtained cepstrum, and a speech judgment section 32 for detecting a speech signal interval based on the detected peak information. The pitch shown in Fig. 4b represents a peak of the speech signal, and a time interval when the peak exits in the cepstrum is a speech signal interval. The peak is detected by, for example, presetting a predetermined threshold value for a cepstrum and comparing respective power levels of the cepstrum with the preset threshold value. At a time when a speech signal interval is detected by the signal judgment section 3, a control signal is outputted from the signal judgment section 3 to the storage unit 2, and then, feature information previously stored in the storage unit 2 is inputted to the maximum likelihood estimator 4. On the other hand, the feature information of the cepstrum analysis result of the speech is inputted from the cepstrum analysis section 1 to the maximum likelihood estimator 4. The maximum likelihood estimator 4 compares the inputted cepstrum analysis result with the feature information outputted from the storage unit 2, sequentially, switching the feature information in an order of the feature information of the speakers A, B, C, D and so on, searching the feature information which is the most like inputted cepstrum analysis result, and detecting which speaker corresponds to the inputted speech. As a method for detecting the degree of the similarity therebetween, there can be used a pattern matching method, a method for detecting a degree of the similarity between amplitudes thereof, and a method for detecting a degree of the similarity between phases thereof, which are well known to those skilled in the art. The signal output section 5 outputs a pair of the detection results in response to the detection result outputted from the signal judgment section 3 and the detection result outputted from the maximum likelihood estimator 4. Namely, the signal output section 5 outputs not only presence information of the speech or information of the speak signal interval but also information of the speaker of the speech signal. An operation of the signal detection apparatus will be described below. First of all, cepstrum analysis results of speeches of respective speakers are previously stored in the storage unit 2. Thereafter, a noisy speech signal is inputted to the cepstrum analysis section 1, and then, the cepstrum analysis section 1 performs the cepstrum analysis for the inputted noisy speech signal as described above. It is to be noted that the cepstrum analysis of speeches of respective speakers may be performed by the cepstrum analysis section 1 as indicated by a line 6 (Fig.1). The signal judgment section 3 detects a speech signal interval of the inputted noisy speech signal utilizing the peak etc. of the cepstrum analysis result outputted from the cepstrum analysis section 1. Then, at a timing when a speech signal interval is detected by the signal judgment section 3, a control signal is outputted from the signal judgment section 3 to the storage unit 2 as indicated by a line 7, and then, the cepstrum analysis result stored in the storage unit 2 is outputted to the maximum likelihood estimator 4. On the other hand, the cepstrum analysis result is inputted from the cepstrum analysis section 1 to the maximum likelihood estimator 4. The maximum likelihood estimator 4 compares the cepstrum analysis result inputted from the cepstrum analysis section 1 with the cepstrum analysis results inputted from the storage unit 2, and determines one of the cepstrum analysis results stored in the storage unit 2 which is the most like cepstrum analysis result outputted from the cepstrum analysis section 1. If there is not any one of the cepstrum analysis results which is like cepstrum analysis result outputted from the cepstrum analysis section 1 with a similarity larger than a threshold value, the maximum likelihood estimator 4 judges that there is no cepstrum analysis result which is like cepstrum analysis result of the inputted speech signal. Finally, the signal output section 5 outputs a pair of the detection result outputted from the maximum likelihood estimator 4 and the detection result outputted from the signal judgment section 3. In the description, a speech signal is used as an example of an input signal, however, the present invention is not limited to this. In stead of the speech signal, there may be used a signal of a sound of an automobile or a signal of a sound of an airplane etc., and then, the other signals are judged as noise. In this case, the cepstrum analysis result is not used, and the other feature information thereof is used. Fig. 2 shows a second signal detection apparatus, and the same components as those shown in Fig. 1 are denoted by the same references as those shown in Fig. 1. As shown in Fig. 2, in a manner similar to that of Fig. 1, the signal judgment section 3 comprises the peak detection section 31 and the speech judgment section 32. However, the apparatus further comprises a noise prediction section 8 and a noise power calculation section 9 in addition to the respective sections of the first apparatus. Differences between both will be described below in detail. The maximum likelihood estimator 4 detects a kind of a speech which is the most like that of an inputted speech signal taking into consideration a predicted noise in the speech in addition to the cepstrum analysis results outputted from the storage unit 2 and the cepstrum analysis result outputted from the cepstrum analysis section 1. Namely, when the speech judgment section 32 detects a time interval during which speech is present, the detection result is outputted from the speech judgment section 32 to the noise prediction section 8. On the other hand, a noisy speech signal is inputted to the noise prediction section 8. Since a portion where there is no speech is a portion where there is only noise (referred to as a noise alone portion hereinafter), the noise prediction section 8 predicts a noise of a speech based on data of the noise alone portion. Namely, the noise prediction section 8, for example, predicts a noise component by respective channels based on the noisy speech signal which are divided into plural m band channels, each band channel having a predetermined band width and respective band channels having bands which are different from each other. Fig. 5 shows one example of a noise prediction method. In Fig. 5, an x-axis is a frequency, a y-axis is a power level of an inputted speech signal, and a z-axis is a time. In a frequency f1, there is data p1, p2, ..., pi as a time is elapsed, and then, data pj presenting after data pi on line of data p1, p2, ..., pi is predicted. For example, an average value of noise data p1 to pi is calculated, and then, the calculated average value is set at a predicted value pj. Alternatively, when a speech signal continuously exits, data pj is multiplied by an attenuation factor, and then, the product of the data pj and the attenuation factor is set at a predicted value. The noise power calculation section 9 calculates a power level of the predicted noise such as an average value of an amplitude thereof. When the calculated power level of the predicted noise is larger than a predetermined threshold value, even though the cepstrum analysis result stored in the storage unit 2 is slightly different from the cepstrum analysis result outputted from the cepstrum analysis section 1, the maximum likelihood estimator 4 judges that they are substantially similar to each other, and the level of the similarity judgment standard is lowered. On the other hand, when the power level of the predicted noise is smaller than the predetermined threshold value, the maximum likelihood estimator 4 judges that there is substantially no noise, and then, the level of the similarity judgment standard is heightened. Fig. 3 shows a noise suppression apparatus, and the same components as those shown in Figs. 1 and 2 are denoted by the same references as those shown in Figs. 1 and 2. The noise suppression apparatus is characterized in outputting a noise-suppressed speech signal utilizing a prediction value of a predicted noise, and further comprises a band division section 10, a cancellation section 11 and a band combining section 12 in addition to the second apparatus. Referring to Fig. 3, a noisy speech signal is inputted to the band division section 10, and then, in response thereto, the band division section 10 converts the inputted analogue noisy speech signal into digital noisy speech signal, Fourier-transforms the digital noisy speech signal into a spectrum signal, and then, divides the transformed spectrum signal into spectrum signals of plural m channels. The spectrum signals of plural m channels outputted from the band division section 10 are inputted to the above-mentioned cepstrum analysis section 1 and the noise prediction section 8. The noisy spectrum signals of respective frequency channels outputted from the band division section 10 is inputted to the cancellation section 11, and the cancellation section 11 rejects or cancels a noise of a prediction value predicted by the noise prediction section 8 as follows. Fig. 6 shows one example of a general cancellation method on a time axis. As shown in Fig. 6, a noise waveform (b) predicted by the noise prediction section 8 is subtracted from a noisy speech signal (a), thereby extracting only a speech signal (c). In the present invention, there is used a cancellation method on a frequency axis shown in Fig. 7. Referring to Fig. 7, a noisy speech signal (a) is Fourier-transformed into a spectrum signal (b), and then, a spectrum (c) of the predicted noise is subtracted from the converted spectrum signal (b) so as to obtain the subtracted spectrum result (d). Further, the subtracted spectrum result (d) is inverse-Fourier-transformed into a speech signal (e) including no noise or a noise-suppressed speech signal (e). The band combining section 12 combines the spectrum signals of plural m channels outputted from the cancellation section 11, and inverse-Fourier-transforms the combined spectrum signals into a noise-suppressed speech signal. As described above, a noise component is canceled from the noisy speech signal by the cancellation section 11, and a speech signal is obtained by the band combining section 12. Therefore, a noise-suppressed speech signal can be obtained together with information of a speaker of the speech signal. It is to be noted that, in the present invention, as features to be stored in the storage unit 2, there is selected peak information of the cepstrum analysis results of respective speakers. Fig. 8 shows a speech recognition apparatus of a first preferred embodiment according to the present invention. The speech recognition apparatus comprises a band division section 401, a cepstrum analysis section 402, a peak frequency detection section 403, a first storage unit 404, a first comparator 405, a speech cutting-out section 406, a feature extraction section 407, a second comparator 408, a second storage unit 409 and a comparison output combining section 410. Referring to Fig. 8, the band division section 401 performs a band division process including a Fourier transformation for an inputted speech signal, and outputs spectrum signals of plural m channels for which the band division process has been performed. For example, the band division section 401 comprises an analogue to digital converter, an FFT for Fourier-converting the A/D converted digital signal to a spectrum signal and a band divider for dividing the spectrum signal into band-divided spectrum signals of plural m channels. The cepstrum analysis section 402 performs a cepstrum analysis for the spectrum signals of plural m channels inputted from the band division section 401, and outputs the cepstrum analysis result to. the peak frequency detection section 403. The peak frequency detection section 403 detects a peak frequency from the cepstrum analysis result outputted from the cepstrum analysis section 402, and outputs the detected peak frequency to the first storage unit 404 and the first storage unit 405. The first storage unit 404 previously stores peak frequencies of respective speakers which are previously obtained by performing the band division process, performing the cepstrum analysis for the band-divided spectrum signals and detecting a peak frequency from the cepstrum analysis result. The first storage unit 404 is constituted by, for example, ROM etc.. The first comparator 404 compares the peak frequency outputted from the peak frequency detection section 403 with the peak frequencies of respective speakers which are stored in the first storage unit 404 and are read out therefrom, and detects which speaker an inputted speech signal corresponds to. When the detected peak frequency of the inputted speech signal is closest to a peak frequency stored in the first storage unit 404 within a permissible range, the first comparator 405 judges that the speaker of the closest peak frequency is a speaker of the inputted speech signal. The comparison result is outputted from the first comparator 405 to the comparison output combining section 410. On the other hand, the second storage unit 409 previously stores speech features of respective speakers, and is constituted by a ROM etc.. For example, the second storage unit 409 stores the speech features of respective speakers by a word, by a syllable such as "a", "i", "u", or by a phoneme. The speech cutting-out section 406 cuts out a speech from a noisy speech signal, and is constituted by a filter. The feature extraction section 407 extracts speech features from the cut-out speech and outputs the speech features to the second comparator 408 and the second storage unit 409. The second comparator 408 recognizes a speech by comparing the speech features extracted by the feature extraction section 407 with the speech features stored in the second storage unit 409, and outputs the comparison result to the comparison output combining section 410. For example, a pattern matching method is used in the second comparator 408. Based on the comparison result outputted from the first comparator 405 and the comparison result outputted from the second comparator 408, the comparison combining section 410 performs a predetermined process and outputs the processed result. For example, when a speech of a speaker which is different from the speakers previously specified is recognized, the recognition result is not outputted from the comparison output combining section 410. Alternatively, the recognition result is outputted together with the speaker information from the comparison output combining section 410. An operation of the speech recognition apparatus of the first preferred embodiment will be described below in detail. Peak frequencies of respective speakers obtained by previously performing the band division process for speech signals of known speakers and performing the cepstrum analysis are previously stored in the first storage unit 404. Speech features of respective speakers are previously stored in the second storage unit 409. Thereafter, the band division section 401 performs the band division process including a Fourier transformation for an inputted unknown noisy speech signal, and then, the cepstrum analysis section 402 performs the cepstrum analysis for the output of the band division section 401. Thereafter, the peak frequency detection section 403 detects a peak frequency from the cepstrum analysis result outputted from the cepstrum analysis section 402, and the first comparator 405 compares the detected peak frequency outputted from the peak frequency detection section 403 with the peak frequencies of respective speakers stored in the first storage unit 404, and detects which speaker the inputted known speech signal corresponds to. The speech cutting-out section 406 cuts out a speech from a noisy speech signal, and the feature extraction section 407 extracts speech features of the cut-out speech. Thereafter, the second comparator 408 recognizes a speech by comparing the speech features extracted by the feature extraction section 407 with the speech features stored in the second storage unit 409. Thereafter, based on the comparison result of the first comparator 405 and the comparison result of the second comparator 408, the comparison output combining section 410 outputs the speech recognition result only when the speaker of the inputted speech signal is a predetermined speaker. Fig. 9 shows a speech recognition apparatus of a second preferred embodiment according to the present invention. In Fig. 9, the same components as those shown in Fig. 8 are denoted by the same references as those shown in Fig. 8. In the present preferred embodiment, the band division section 401, the cepstrum analysis section 402, the first storage unit 404, the first comparator 405, the speech cutting-out section 406, the feature extraction section 407, the second comparator 408, the second storage unit 409 and the comparison output combining section 410 are constituted in manners similar to those shown in Fig. 8, the description of these sections is omitted therein. A peak detection section 411, a speech judgment section 412, a noise prediction section 413 and a cancellation section 414 are constituted in manners similar to those of the peak detection section 31, the speech judgement section 32, the noise prediction section 8 and the cancellation section 11 as described in connection with Fig. 3. Furthermore, a peak frequency detection section 403 is provided, which detects a peak or pitch frequency of the cepstrum analysis result. In the present preferred embodiment, the cancellation section 414 outputs noise-suppressed spectrum signals of plural m channels in a manner of that of the cancellation section 11 shown in Fig. 3 by canceling a noise from the noisy spectrum signals. A band combining section 415 performs the band combining process including the inverse Fourier transformation for the spectrum signals of plural m channels outputted from the cancellation section 414, and outputs a transformed speech signal to the speech cutting-out section 406. The speech cutting-out section 406 cuts out a speech from the speech signal outputted from the band combining section 415. In the present preferred embodiment, as described above, since a noise is canceled from a noisy speech signal for which the speech recognition process is performed, the speech recognition process can be performed in a recognition rate higher than that of the conventional speech recognition apparatus. In the above-mentioned preferred embodiments, the cepstrum analysis section, the maximum likelihood detection section, the noise interval judgment section, the peak frequency detection section, the noise prediction section, the speech detection section, the formant analysis section and the first and second comparators are constituted utilizing a computer by software. However, these sections can also be constituted by hardware only used for these sections. In the preferred embodiments, a speech signal may be handled as a noise. Namely, in the present invention, a noise means a signal other than a specified signal. In the above-mentioned preferred embodiments, there is provided plural comb type band-pass filters. However, only one comb type band-pass filter may be provided, and then, may be controlled by a time division multiplex method.

Claims: A speech recognition apparatus comprising: first storage means (404) for peak frequencies of plural speakers which are previously obtained by performing a band division process including a Fourier transformation for speech signals of plural speakers and performing a cepstrum analysis process for the transformed speech signals; band division means (401) for performing a band division process including a Fourier transformation for an inputted noisy speech signal and outputting spectrum signals of plural channels; cepstrum analysis means (402) for performing a cepstrum analysis process for the spectrum signals of plural channels outputted from said band division means (401) and outputting a cepstrum analysis result; peak frequency detection means (403) for detecting a peak frequency in response to the cepstrum analysis result outputted from said cepstrum analysis means (402); first comparison means (405) for detecting which speaker the inputted noisy speech signal corresponds to by comparing the peak frequency detected by said peak frequency detection means (403) with the peak frequencies stored in said first storage means (404), and outputting a first comparison result; second storage means (409) for storing predetermined features of speeches of plural speakers; speech cutting-out means (406) for cutting out a speech from the inputted noisy speech signal; feature extraction means (407) for extracting a feature of the speech cut out by said speech cutting-out means (406); second comparison means (408) for recognizing a speech by comparing the feature of the speech extracted by said feature extraction means (407) with the features of the speeches stored in the second storage means (409) outputting a second comparison result; and comparison combining means (410) for performing a predetermined process for the first comparison result outputted from said first comparison means (405) and the second comparison result outputted from said second comparison means (408). The speech recognition apparatus as claimed in claim 1, further comprising: speech judgment means (412) for detecting a speech signal interval in response to the cepstrum analysis result outputted from said cepstrum analysis means (402); noise prediction means (413) for predicting a noise of a speech in the spectrum signals of plural channels outputted from said band division means (401) according to the speech signal interval detected by said speech judgment means (412); cancellation means (414) for cancelling the noise predicted by said noise prediction means (413) in the spectrum signals of plural channels and outputting the noise-cancelled spectrums signals of plural channels; and band combining means (415) for combining the noise-cancelled spectrum signals of plural channels outputted from said cancellation means (414), inverse-Fourier-transforming the combined spectrum signal, and outputting a transformed speech signal; wherein said speech cutting-out means (406) cuts out a speech in the transformed speech signal outputted from said band combining means (415) and outputting the cut-out speech to said feature extraction means (407).

Inventors: KANE JOJI, NOHARA AKIRA, KANE, JOJI, NOHARA, AKIRA
Applicants: MATSUSHITA ELECTRIC IND CO LTD, MATSUSHITA ELECTRIC INDUSTRIAL CO., LTD.
Application year: 1991
Main classification: G10L21
IPC codes: G10L11, G10L21