Speech recognizing performing noise adaptation

Abstract: It is an object of the invention to remove an additive noise due to an influence or the like by an ambient circumstance in a real-time manner in order to improve precision of a speech recognition which is performed in a real-time manner. For this purpose, a converting process into a noise superimposed speech model is selectively performed to a speech model of a phoneme which is specified by a searching state and a recognition grammar during the search of a speech and can become a search target. A likelihood calculation for recognition of an input speech is executed by using the noise superimposed speech model formed by the converting process.

Description: BACKGROUND OF THE INVENTION Field of the Invention It is an object of the invention to perform a speech recognition by using a Hidden Markov Model (HMM). Another object of the invention is to remove an additive noise from an input speech. Related Background Art In case of performing a speech recognition in a real environment, there is a problem of a noise as one of large problems. Although the noise is an additive noise which is additively added to spectrum characteristics, there is a Parallel Model Combination (PMC) method as a method which is effective for the additive noise. The PMC method has been described in detail in M. J. Gales and S. Young, "An Improved Approach to the Hidden Markov Model Decomposition of Speech and Noise", Proc. of ICASSP'92, I-233 - 236, 1992. The PMC method is a method of adding and synthesizing an HMM (speech HMM) learned from speech collected and recorded in a noiseless environment and an HMM (noise HMM) learned from noise, thereby approaching a model to a noise superimposed environment and executing a conversion to add a noise to all of the models. In a noise process in the PMC, it is presumed that an additiveness of noise and speech is established in a linear spectrum region. On the other hand, in the HMM, parameters of a logarithm spectrum system such as a cepstrum and the like are often used as a characteristic amount of the speech. According to the PMC method, those parameters are converted into the linear spectrum region and are added and synthesized in the linear spectrum region of the characteristic amount which is derived from the speech HMM and noise HMM. After the speech and the noise are synthesized, an inverse conversion is performed to return the synthesized value from the linear spectrum region to the cepstrum region, thereby obtaining a noise superimposed speech HMM. By using the foregoing PMC method, it is possible to cope with additive noises such as internal noise, background noise, and the like. However, the PMC method has problems such that since a nonlinear conversion is executed to all of the models, a calculation amount is large, a processing time is very long, and it is not suitable for an instantaneous environment adaptation in which an adaptation to the noise is performed simultaneously with the recognition. It is known from MATSUI T ET AL: 'N-best-based instantaneous speaker adaptation method for speech recognition', PROCEEDINGS of ICSLP '96, FOURTH INTERNATIONAL CONFERENCE ON SPOKEN LANGUAGE PROCESSING (CAT. NO. 96TH8206), PHILADELPHIA, PA, USA, 3 - 6 OCT. 1996, pages 973-976 vol. 2, XP002091179 ISBN 0-7803-3555-4, 1996, New York, NY, USA, IEEE, USA to provide HMM based recognition using a multiple pass search strategy and to iteratively adapt all phoneme models to be speaker dependent. It is known from GALES M J F ET AL: 'AN IMPROVED APPROACH TO THE HIDDEN MARKOV MODEL DECOMPOSITION OF SPEECH AND NOISE', PROCEEDINGS OF ICASSP '92, SPEECH PROCESSING 1, SAN FRANCISCO, MAR. 23 - 26, 1992, vol. 1, no. CONF. 17, 23 March 1992, pages 233-236, XP000341126, INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS to combine models based on clean speech and noise to form a noise adapted HMM model in the cepstral domain. Aspects of the invention are set out in the appended claims. According to an embodiment, an adaptation time can be remarkably reduced as compared with the conventional case of performing the noise adaptation by the PMC to all of the models. In particular, even in the case where the number of models increases like phoneme models of an environment depending type, it is possible to cope with such a situation in a short time. Moreover, since an adapting process is executed in a recognizing step, the adaptation and the recognition can be simultaneously performed. Further, by using a high speed PMC method, even in the case where the number of mixed models increases, it is possible to cope with such a situation by a small calculation amount. BRIEF DESCRIPTION OF THE DRAWINGS Fig. 1 is a functional block constructional diagram of an apparatus according to the invention; Fig. 2 is a flowchart for a speech recognizing method using a calculating method of a PMC using a backward search by a multipath search; Fig. 3 is a flowchart for a speech recognizing method using a calculating method of a PMC using an on-demand process; Fig. 4 is a flowchart for a speech recognizing method using a high speed PMC method; and Fig. 5 is a constructional block diagram of the apparatus according to the invention. Fig. 6 shows a memory map of a memory medium according to the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS An embodiment according to the invention will now be described in detail hereinbelow with reference to the drawings. Fig. 5 is a block diagram showing a construction of a speech recognizing apparatus according to an embodiment of the invention. Reference numeral 101 denotes a display unit such as CRT, liquid crystal display, or the like for displaying a character train obtained as a result of a speech recognition; 102 a print unit such as LBP, ink jet printer, or the like for printing the character train obtained as a result of the speech recognition; and 103 a speech input unit such as a microphone or the like. Speech can be also inputted via a public line or the like or the LAN as a speech input unit 103. Reference numeral 104 denotes a CPU for controlling so as to execute various processes, which will be explained hereinlater, in accordance with control programs stored in a memory unit 105 or a detachable storing medium 107 such as a CD-ROM or the like. Although the CPU 104 also controls the execution of various applications in addition to processes, which will be explained hereinlater, the CPU 104 controls (for example, execution of commands such as "print", "end", and the like) so as to execute various processes regarding the applications in accordance with a command corresponding to the speech recognized by the method of the invention. Reference numeral 105 denotes the memory unit provided in the apparatus. The memory unit 105 stores control programs for various processes which are executed by the CPU 104 and will be explained hereinlater, various parameters (for example, data corresponding to a dictionary which is used for the speech recognition, and the like) which are necessary for the control programs, speech data to be recognized, and the like. The control programs and various parameters which are stored in the memory unit 105 can be inputted from the storing medium 107 or can be also inputted and stored through a communication line. Reference numeral 106 denotes a communication I/F for controlling a communication so as to transmit and receive data through communicating means such as public line, LAN, or the like. It is also possible to construct in a manner such that speech inputted by another apparatus or control programs and various parameters stored in the other apparatuses are fetched into the apparatus via the communication I/F 106 and are stored into the memory unit 105, and after that, each process which will be explained hereinlater is started. Reference numeral 107 denotes the storing medium such as CD-ROM, FD, or the like which can be attached and detached to/from the apparatus main body, namely, the storing medium which can store the control programs, various parameters, and speech data which have already been described as being stored in the memory unit 105 and can be attached and detached to/from the apparatus main body. The data can be downloaded from the storing medium 107 to the memory unit 105 before starting various processes, which will be explained hereinlater, or the CPU 104 can also directly access to the storing medium 107. Fig. 6 is a memory map of the data stored in the memory unit 105 or storing medium 107. Control programs for various processes as shown at 401 to 403 have been stored in the memory map. Although not shown in Fig. 6, a parameter storing portion, a speech data storing portion, a working area, and the like besides them are also provided. Fig. 1 is a functional block constructional diagram of an apparatus according to the invention. In the diagram, reference numeral 1 denotes a microphone for inputting speech. The microphone 1 is realized by the speech input unit 103. Reference numeral 2 denotes a speech analyzer for converting the inputted speech to a feature parameter time series; 3 a searcher for searching a correct recognition series by using information of feature parameters, grammar, and phoneme models. The speech analyzer 2 and searcher 3 are realized by the CPU 104. Reference numeral 4 denotes a recognition grammar and 5 indicates a phoneme model for speech recognition. The recognition grammar 4 and phoneme model 5 for speech recognition are realized by the data stored in the memory unit 105 or storing medium 107. First, 1) a calculating method of a PMC using a backward search by a multipath search will be described with reference to Fig. 1 and a flowchart of Fig. 2. The multipath searching method is a method of narrowing correct candidates by repeating the search a plurality of times. For example, in a method of outputting a recognition result after completion of the search of two times of a forward search and a backward search, phoneme models in which the adaptation by the PMC is not performed are used at the time of the forward search, the PMC is performed to only the phoneme models necessary for recognition at the time of the backward search, and a likelihood calculation is executed by the adapted phoneme models. The speech inputted by the microphone 1 is converted into a time sequence of a feature amount of the speech such as a cepstrum or the like by a speech analysis in the speech analyzer 2. The multipath search is used in the searcher 3. There is a Tree-trellis based search or a Forward-backward search as a method of recognizing by the multipath search. The Tree-trellis based search has been disclosed in detail in the literature by F. K. Soong and E. F. Huang, "A Tree-Trellis Based Fast Search for Finding the N Best Sentence Hypoteses in Continuous Speech Recognition", Proc. of ICASSP91, pp.705 - 708, May, 1991. The Forward-backward search has been described in detail in the literature by S. Austin, R. Schwartz, and P. Placeway, "The Forward-Backward Search Algorithm", Proc. of ICASSP91, pp. 697 - 700, May, 1991. Although an example using the Tree-trellis based search will be described here, the searching method of a searcher is not limited to such an example. The Tree-trellis based search method is an algorithm for realizing a strict N-best search and is constructed by searches of two stages of the forward search and the backward search. In the forward search (the first search), a Viterbi search synchronized with a frame is performed and an upper predetermined number of results among the results obtained by the forward search are selected as recognition candidates by the first search of the input speech. In the backward search (the second search), an A* search in which the results of the forward search are heuristic is performed with respect to each of the selected candidates. In the searches of two stages, a likelihood calculation is executed by using phoneme models before the adaptation by the PMC at the time of the forward search, thereby narrowing the recognition candidates from the grammar (22). The adaptation by the PMC is performed (23) at the time of the backward search (24) with respect to only the phoneme models necessary for recognition of the narrowed recognition candidates, namely, with regard to only the phoneme models constructing the recognition candidates narrowed in (22). The likelihood is calculated by using the phoneme models after the adaptation, thereby obtaining N-best recognition candidates. Since the likelihood at the time of the forward search and the likelihood at the time of the backward search are different, the A* condition is not satisfied. To obtain a more correct result, the recognition candidates are sorted by the likelihoods (25), thereby obtaining a final recognition result (26). 2) A method using an on-demand process will now be described hereinbelow with reference to a flowchart of Fig. 3. The method using the on-demand process is a method of performing the PMC with respect to only phonemes which are necessary for recognition in the first searching step and executing a likelihood calculation by the adapted phoneme model. The processes in the speech input unit 1 and speech analyzer 2 are similar to those in Fig. 2 and the Tree-trellis based search is used for the searching process of the searcher 3 in a manner similar to the case of 1). However, after the adaptation by the PMC was performed (32) with respect to only the phonemes necessary for the likelihood calculation in the forward search (33), the likelihood calculation is executed. The phonemes which are necessary in the forward search are the phonemes which are narrowed as candidates during the search by the search result before then and the recognition grammar 4. The recognition candidates narrowed in the grammar by the process of (33) and the result of the likelihood calculation are stored in the memory unit 105. In the backward search, the result of the likelihood calculation stored in the memory unit 105 is read out and a logarithm likelihood obtained from this value is added, thereby searching (34). A final recognition result is obtained and outputted (35). In the processes described above, as for the PMC in (23) or (32), the adaptation can be usually performed for every state and every mixed element of each phoneme model. However, a high speed PMC method of synthesizing the mixed elements and converting into one mix and executing the PMC to such one mix can be also used. The high speed PMC method will now be described hereinbelow. (An example of the high speed PMC methods has been also described in Japanese Patent Application No. 8-336291, "Speech Recognizing Apparatus and Speech Recognizing Method" and corresponding EP-A-0847041). 1. A mixed distribution of each state of the phoneme models is converted into one distribution (representative distribution). The conversion is executed by the following equation (1). where, (µ m , σ 2 m ): distribution in the state (µ c , σ 2 c ): representative distribution w: weight coefficient G: state 2. A relative positional relation between the representative distribution and each distribution in the state is obtained. 3. A noise adaptation is executed to the representative distribution by the PMC method. 4. Each distribution after the noise conversion is determined by using the relation between the representative distributions before and after the PMC conversion and the representative distributions and the relative positional relation of each distribution obtained in 2.. A procedure other than the PMC is executed in a manner similar to the processes described in the flowcharts of Figs. 2 and 3. A method of using the above high speed PMC method will now be described with reference to a flowchart of Fig. 4. According to this method, a mixed distribution of each state of the phoneme model necessary for the forward search is first converted into a representative distribution which is expressed by one distribution (42) by using the above equation (1). The phoneme model which is subjected to the conversion to the representative distribution is selected in a manner similar to (32). Subsequently, the representative distribution is adapted to the noise by the PMC method (43). The forward search is subsequently executed. However, different from (22) in Fig. 2 and (33) in Fig. 3, the likelihood calculation in this case is executed by using the representative distribution after it was adapted to the noise by the PMC (44). After completion of the forward search as mentioned above, the position of each mixed distribution after the noise adaptation is obtained (45) by using the relative positional relation between the representative distribution which has already been obtained before the conversion to the representative distribution as mentioned above and each distribution. The likelihood calculation is executed by using the obtained mixed distribution after the adaptation and the backward search is performed (46). In the embodiment, since the likelihood of the forward search and that of the backward search differ, the recognition results are sorted on the basis of the likelihood (47) and the final recognition result is obtained and outputted (48).

Claims: A speech recognizing method for recognizing input speech by using hidden Markov models as speech models (5), comprising the steps of: forming (23) a noise superimposed speech model by the steps of: converting the speech model into a linear spectrum dimension; adding a noise model to the speech model in the linear spectrum dimension; and converting the addition result into a cepstrum dimension to form the noise superimposed speech model; the method further comprising performing a likelihood calculation to recognize the input speech by using the noise superimposed speech models, the likelihood calculation comprising: performing a first searching process (22) to obtain recognition candidates by using speech models to which no noise is superimposed; performing the process of forming noise superimposed speech models (23) with respect to only those speech models necessary for recognition of said recognition candidates obtained by the first searching process; and performing a second searching process (24) on said recognition candidates obtained by the first searching process by using said noise superimposed speech models. A method according to claim 1, wherein: a mixed continuous distribution type is used as said hidden Markov model that is used as said speech model; a mixed distribution of the speech model is synthesized to one distribution in the step of forming said noise superimposed speech model and said noise superimposed speech model is formed from said synthesized speech model; and said formed noise superimposed speech model is decomposed to the mixed distribution and the decomposed mixed distribution is set to a noise superimposed speech model of the mixed continuous distribution type. A method according to claim 1, wherein: a mixed continuous distribution type is used as said hidden Markov model that is used as said speech model; in the process for selectively forming the noise superimposed speech model from speech models which can become candidates in the first searching process, a mixed distribution of the speech model is synthesized to one distribution and said noise superimposed speech model is formed from said synthesized model and stored into a memory unit; the likelihood calculation and the search are performed by using said formed noise superimposed speech model of a single distribution type; in the second searching process which is executed after said first searching process, the noise superimposed speech model of the single distribution type of the speech model which is subjected to the likelihood calculation is read out from said memory unit and said read-out noise superimposed speech model is decomposed to the mixed distribution; and said second searching process is performed by using said noise superimposed speech model of the mixed distribution type obtained by decomposing. A method according to claim 1, further comprising the step of displaying recognition candidate characters obtained by said search on a display. A method according to claim 1, further comprising the step of printing recognition candidate characters obtained by said search by printing means. A method according to claim 1, wherein a speech inputted from a microphone is recognized by said searching process. A method according to claim 1, wherein said speech is inputted through a communication line. A method according to claim 1, wherein an operation control of an application is performed in accordance with a recognition result obtained by said search. A speech recognizing apparatus (101-107) for recognizing input speech by using hidden Markov models as speech models (5), comprising: noise superimposed speech model forming means (403) comprising: converting means for converting a speech model into a linear spectrum dimension; adding means for adding a noise model to the speech model in the linear spectrum dimension; and converting means for converting the addition result into a cepstrum dimension to form the noise superimposed speech model; the apparatus further comprising calculating means for performing a likelihood calculation to recognize the input speech by using the noise superimposed speech models; the likelihood calculation means comprising: first searching means (401) for performing a first searching process (22) to obtain recognition candidates by using speech models to which no noise is superimposed; said noise superimposing speech model forming means forming noise superimposed speech models with respect to only those speech models necessary for recognition of said recognition candidates obtained by the first searching process; and said apparatus further comprising second searching means (402) for performing a second searching process (24) on said recognition candidates obtained by the first searching process by using said noise superimposed speech models. An apparatus according to claim 9, further comprising: holding means for holding a likelihood value obtained by said calculating means by using the noise superimposed speech model in the search at the first time; and likelihood value deciding means for deciding a likelihood value of the search at the second time by adding the likelihood value held in said holding means in the search at the second time. An apparatus according to claim 9, wherein: a mixed continuous distribution type is used as said hidden Markov model that is used as said speech model; and said noise superimposed speech model forming means synthesizes a mixed distribution of the speech model to one distribution, forms said noise superimposed speech model from said synthesized speech model, decomposes said formed noise superimposed speech model to the mixed distribution, and forms a noise superimposed speech model of the mixed continuous distribution type. An apparatus according to claim 9, wherein: a mixed continuous distribution type is used as said hidden Markov model that is used as said speech model; said noise superimposed speech model forming means synthesizes a mixed distribution of speech models which can become candidates to one distribution, forms a noise superimposed speech model of a single distribution type, decomposes said noise superimposed speech model of the single distribution type to the mixed distribution, and forms a noise superimposed speech model of a mixed distribution type; in the first search, the search is performed by using said formed noise superimposed speech model; in the second search which is executed after said first search, the search is performed by using the noise superimposed speech model of the mixed distribution type of the speech model which is subjected to the likelihood calculation. An apparatus according to claim 9, further comprising display means (101) for displaying recognition candidate characters obtained by said search. An apparatus according to claim 9, further comprising printing means (102) for printing recognition candidate characters obtained by said search. An apparatus according to claim 9, further comprising a microphone (103) for inputting a speech as said search target. An apparatus according to claim 9, further comprising a communication line interface (106) for inputting a speech as said search target. An apparatus according to claim 9, further having control means (104) for performing an operation control of an application in accordance with a recognition result obtained by said search. A data carrier (107) storing program instructions for implementing the method according to any one of claims 1 to 8 when run on a computer (104).

Inventors: KOSAKA TETSUO, YASUHIRO KOMORI, KOSAKA, TETSUO, YASUHIRO, KOMORI
Applicants: CANON KK, CANON KABUSHIKI KAISHA
Application year: 1998
Main classification: G10L15
IPC codes: G10L15