Speech recognizing method and apparatus

Abstract: Speech including a speech portion and a non-speech portion is inputted, a Cepstrum long time mean of the speech portion is obtained from the speech portion of the input speech, a Cepstrum long time mean of the non-speech portion is obtained from the non-speech portion of the input speech, each Cepstrum long time mean is converted from a Cepstrum region to a linear region, and after that, it is subtracted on a linear spectrum dimension, the subtracted mean is converted into a Cepstrum dimension, a Cepstrum long time mean of a speech portion in a speech database for learning is subtracted from the converted result, and the subtracted result is added to a speech model expressed by Cepstrum. Thus, even when a noise is large, a presuming precision of a line fluctuation is raised and a recognition rate can be improved.

Description: The invention relates to speech recognizing method and apparatus for enabling a speech recognition of a high recognition rate by eliminating a distortion by line characteristics and an influence by an internal noise. The invention also relates to speech recognizing method and apparatus for performing a speech recognition by using a hidden Markov model (HMM). In case of performing a speech recognition in an actual environment, a distortion of line characteristics by influences by a microphone, telephone line characteristics, or the like and an additive noise such as an internal noise or the like particularly cause problems. As methods of coping with those problems, a Cepstrum Mean Subtraction (CMS) method and a Parallel Model Combination (PMC) method have been proposed so far. The CMS method has been described in detail in Rahim et al., "Signal Bias Removal for Robust Telephone Based Speech Recognition in Adverse Environments", Proc. of ICASSP, '94, (1994) or the like. The PMC method has been described in detail in M. J. Gales, S. Young, "An Improved Approach to the Hidden Markov Model Decomposition of Speech and Noise", Proc. of ICASSP, '92, I-233-236, (1992). The CMS method is a method of compensating a distortion of line characteristics. On the other hand, the PMC method is a method of coping with the additive noise. In both of those methods, a noise portion and a speech portion are detected from an input speech, and a Hidden Markov Model (HMM) formed in an environment without a line distortion and a noise is corrected on the basis of those information, thereby allowing it to be adapted to the input environment. With this method, even if the line characteristics or noise fluctuate, it is possible to flexibly cope with such a case. The CMS method is a method of compensating a multiplicative noise (line distortion) which acts by a convolution of an impulse response. A long time spectrum of an input speech is subtracted from the input speech and a long time spectrum of a speech used to form a model is subtracted from the model, thereby normalizing a difference between line characteristics. The normalizing process is generally executed in a logarithm spectrum region or Cepstrum region. Since the multiplicative noise appears as an additive distortion in those two regions, the noise can be compensated by a subtraction. A method of performing the normalizing process in the Cepstrum region between those methods is called a CMS method. The PMC method is a method of adding and synthesizing an HMM (speech HMM) learned by a speech recorded in a noiseless environment and an HMM (noise HMM) learned by a noise, thereby making a model further approach a noise multiplexed environment. In the noise process in the PMC, it is presumed that an additiveness of the noise and speech is satisfied in a linear spectrum region. On the other hand, in the HMM, parameters of a logarithm spectrum system such as Cepstrum or the like are often used as a feature amount of a speech. According to the PMC method, those parameters are converted into the linear spectrum region and feature amounts which are obtained from the speech HMM and noise HMM are added and synthesized in the linear spectrum region. After the speech and noise were synthesized, by performing an inverse conversion, the region is returned from the linear spectrum region to the Cepstrum region, thereby obtaining a noise multiplexed speech HMM. By using the CMS method as mentioned above, it is possible to cope with the distortion of the line characteristics by the influences by the microphone, telephone line characteristics, or the like. By using the PMC method, it is possible to cope with the additive noise such as an internal noise or the like. Therefore, by combining the CMS and PMC, even when there are influences by the line distortion and additive noise, it is possible to simultaneously adapt to them. However, when both of them are simply combined, there is a fear such that when an SNR (signal to quantization noise ratio) is low, the line characteristics cannot be properly presumed. Such a fear is caused by that when the line characteristics are presumed, the additive noise exerts an adverse influence and the line characteristics cannot be properly presumed. Particularly, a large problem occurs when the characteristics of the additive noise is not a white noise. To solve the above problems, according to the invention as claimed in clims 1-23, there are provided speech recognizing method and apparatus in which a speech including a speech portion and a non-speech portion is inputted, a Cepstrum long time mean of the speech portion is obtained from the speech portion included in the input speech, a Cepstrum long time mean of the non-speech portion is obtained from the non-speech portion included in the input speech, the Cepstrum long time mean of the non-speech portion is subtracted from the Cepstrum long time mean of the speech portion on a linear spectrum dimension, after that, the resultant mean is converted into a Cepstrum dimension, a Cepstrum long time mean of a speech portion of a database for learning is subtracted from the converted mean, and the resultant mean is further added to a speech model expressed by Cepstrum. Thus, even when the noise is large, since the Cepstrum long time mean is obtained in consideration of an influence by the noise, a precision of presumption of a line fluctuation is raised, resulting in an improvement of a recognition performance. A number of embodiments of the invention will now be described, by way of example only, with reference to the accompanying drawings in which: Fig. 1 is a block constructional diagram of a speech recognizing apparatus of an embodiment of the invention; Fig. 2 is a block constructional diagram of another speech recognizing apparatus; Fig. 3 is a constructional diagram of the speech recognizing apparatus; and Fig. 4 is a diagram showing a memory map example in a memory. Fig. 3 is a block diagram showing a construction of a speech recognizing apparatus of an embodiment of the invention. Reference numeral 101 denotes a display such as CRT, liquid crystal display, or the like for displaying a character train that is obtained as a result of a speech recognition of the invention; 102 a print unit such as LBP, ink jet printer, or the like for printing the character train that is obtained as a result of the speech recognition in the invention; and 103 a speech input unit such as a microphone or the like. A device for inputting a speech through a public line or the like can be also used 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 a control program stored in a memory 105 or a detachable storage medium 107 such as a CD-ROM or the like; and 105 the memory which is provided in the apparatus main body. The memory 105 stores the 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 that is used for speech recognition, and the like) which are necessary for those processes, speech data to be recognized, and the like. Reference numeral 106 denotes a communication interface (I/F) for controlling a communication in order 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 by the communication I/F 106, a speech inputted by another apparatus and control programs and various parameters stored in another apparatus are transmitted to the apparatus main body and stored into the memory 105 and, after that, processes, which will be explained hereinlater, are started. Reference numeral 107 denotes the storage medium which is detachable for a main body of, for instance, a CD-ROM, an FD, or the like. The storage medium is a medium which is detachable for the apparatus main body and can store the control program, various parameters, and speech data which have already been described and stored in the memory 105. Before starting processes, which will be explained hereinlater, data can be downloaded from the storage medium 107 into the memory 105 or the CPU 104 can also directly access to the storage medium 107. Fig. 3 is a memory map of the data stored in the memory 105 or storage medium 107. Control programs for processes as shown at reference numerals 401 to 403 have been stored in the memory map. Although not shown in Fig. 3, the apparatus also has a parameter storing unit, a speech data storing unit, a working area, and the like. The embodiment of the invention will now be described in detail hereinbelow with reference to Fig. 1. Fig. 1 is a functional block constructional diagram of a speech recognizing process of the invention. A flow of data can be understood by Fig. 1. In Fig. 1, reference numeral 1 denotes a CM (Cepstrum long time mean) learning unit for calculating a CM from the data on a speech database for learning. The CM learning unit 1 calculates the CM in accordance with a CM derivation program 401-a. Reference numeral 2 denotes a CM [hereinafter, referred to as a CM(2)] which is thus obtained and stored in the memory 105; 3 an HMM learning unit for obtaining an HMM from the data on the speech database for learning; 4 an HMM [hereinafter, referred to as an HMM(4)] which is thus obtained and stored in the memory 105; 5 a CM learning unit for obtaining a CM from a speech portion included in an input speech and obtaining a CM in accordance with the CM derivation program 401-a; 6 a CM [hereinafter, referred to as a CM(6)] which is thus obtained and stored in the memory 105; 7 a noise HMM learning unit for learning a noise HMM from a non-speech portion included in the input speech; 8 a noise HMM [hereinafter, referred to as an HMM(8)] which is thus obtained and stored in the memory 105; and 9 a conversion unit for converting the CM(6) obtained from the speech portion of the input speech into a linear spectrum region from a Cepstrum region. The conversion in the conversion unit 9 is executed in accordance with a linear spectrum conversion program 401-b. Reference numeral 10 denotes a conversion unit for converting the noise HMM(8) from the Cepstrum region to the linear spectrum region. The conversion in the conversion unit 10 is executed in accordance with the linear spectrum conversion program 401-b. Reference numeral 11 denotes a conversion unit for converting from the linear spectrum region to the Cepstrum region; 12 a conversion unit from the Cepstrum region to the linear spectrum region; 13 a PMC execution unit for executing a PMC in accordance with a PMC data processing program 403; 14 a conversion unit for converting a linear spectrum region expression of a model obtained by the PMC into a Cepstrum region; 15 an HMM [hereinafter, referred to as an HMM(15)] that is converted and finally obtained; and 16 a speech recognition unit for performing a speech recognition by using the HMM(15). The present recognizing apparatus operates as an environment adaptive type for fetching a part of the input speech and performing an adaptation of a model by data fetched. The operation in an environment adaptive mode will be first described. First, the input speech inputted by the speech input unit 103 is divided into a speech portion and a non-speech portion without a speech. It is assumed that the input speech has been converted into a parameter such as a Cepstrum or the like. An HMM for a noise is first learned by the noise HMM learning unit 7 by using the data of the non-speech portion on the basis of an ordinary Baum-Welch algorithm. Thus, the noise HMM(8) can be formed. A long time mean of the Cepstrum of the speech portion of the input speech is calculated by the CM learning unit 5. Each of them is converted from the Cepstrum region to the linear spectrum region. A converting method in this example has been described in detail in the forgoing literature of M. J. Gales, et al. In the conversion in this case, only the mean value is used in the CM(6) of the speech portion and the mean value and divergent value are used with respect to the noise HMM(8). An expression in a linear spectrum region CM(6)' of the CM(6) calculated from the speech portion of the input data is shown below. CM (6)' = x lin An expression of the linear spectrum region of the mean value among parameters of the HMM(8) calculated from the non-speech portion is shown below. HMM (8)' = n lin In the above equation, a superscript "lin" denotes a linear spectrum expression. Subsequently, a calculation shown by the following equation (1) is executed. x' lin = x lin - K 1 n lin A calculation result is converted from the linear spectrum expression to the Cepstrum expression by the conversion unit 11. This conversion is as shown below. x' cep A presumption error of the CM(6) of the input speech can be reduced by the subtraction shown in the equation (1). K 1 denotes a constant and an argument cep shows a Cepstrum expression. Subsequently, the CM(2) is calculated in the CM learning unit 1 by using partial data in the speech database for learning. CM(2) = y cep Likewise, the HMM(4) in which a phoneme, a word, or the like for speech recognition is set to a unit is learned by the HMM learning unit 3 by using partial data in the speech database for learning. A mean value among the parameters of the HMM is shown as follows. HMM(4)' = y(t) cep The above mean value becomes a mean value parameter of the HMM which doesn't correspond to a noise or a line fluctuation before adaptation. Although a speech recognition can be performed by using the HMM(4)', when it is influenced by the noise or line fluctuation, a recognition rate deteriorates. A calculation shown in the following equation (2) is now executed. HMM(4)'' = y (t) cep - y cep + x' cep Thus, the mean value in the HMM(4) obtained by the HMM learning unit 3 is converted and HMM(4)" is obtained. The HMM(4)" obtained here copes with only the line fluctuation in the noise and line fluctuation. The HMM(4)" is converted by the conversion unit 12 from the Cepstrum expression to the linear spectrum expression. In the PMC 13, a constant K 2 is multiplied to a noise HMM(8)' derived from the conversion unit 10 by the PMC execution unit 13. The noise HMM(8)' and the HMM(4)" corresponding to only the line fluctuation are mixed by the PMC method, thereby obtaining an HMM(13). By converting the resultant HMM(13) into the Cepstrum expression by the conversion unit 14, an HMM(14) = (y(t) cep )' adapted to the noise and line fluctuation is obtained. By using the HMM(14) obtained, the speech recognition unit 16 executes a speech recognition by a general speech recognizing method using the HMM. where, (y(t) cep )' = log[K 2 HMM(8)' + HMM(4)"] As an embodiment different from the above embodiment, a method whereby an adaptive mode and a recognition mode are not individually performed will now be described. In the construction of Fig. 1, the adaptive mode and the recognition mode are switched, and individually performed for the speech input for adaptation and the speech input for recognition. On the other hand, a construction in which the adaptive mode is not provided is also possible. Fig. 2 shows such a construction. When a recognition target vocabulary is inputted from the speech input unit 103, the data is divided into a speech portion and a non-speech portion and is transferred to an environment adaptation unit 18. The environment adaptation unit has the same construction as the constructions 5 to 14 described in Fig. 1. The HMM [corresponding to the HMM(4)] before adaptation stored in an HMM 17 is adapted by the environment adaptation unit 18, so that an HMM 19 after the adaptation [corresponding to HMM(14)] is obtained. By using such an HMM, the recognition target vocabulary is recognized by a speech recognition unit 20. Namely, different from the foregoing example, one input speech is used for both of the adaptation and the recognition.

Claims: A speech recognizing method of comprising the steps of: inputting a speech including a speech portion and a non-speech portion; obtaining a Cepstrum long time mean ( x cep ) of the speech portion from the speech portion included in the input speech; obtaining a Cepstrum long time mean ( n cep ) of the non-speech portion from the non-speech portion included in the input speech; subtracting the Cepstrum long time mean of the non-speech portion from the Cepstrum long time mean of the speech portion on a linear spectrum dimension to obtain a first subtracted result ; converting the first subtracted result into a Cepstrum dimension; and subtracting a Cepstrum long time mean ( y cep ) of a speech portion in a speech database for learning from the converted result to obtain a second subtracted result, and adding the second subtracted result to a speech model expressed by a Cepstrum to obtain a modified speech model. A method according to claim 1, wherein the input speech is recognized by using the modified speech model obtained. A method according to claim 1, wherein the Cepstrum long time mean is data of a hidden Markov model. A method according to claim 1, wherein a noise model expressed by Cepstrum parameters and the speech model expressed by the Cepstrum parameters are added and synthesized in a linear spectrum dimension, thereby forming a noise multiplexed model, and a mean value parameter of the noise model is used as a Cepstrum parameter long time mean of the non-speech portion. A method according to claim 1, wherein by using a memory in which a control program of each of the processes has been stored, each of the processes is executed in accordance with the control program. A method according to claim 2, wherein the speech recognition is performed by a hidden Markov model. A method according to claim 2, wherein a control is performed so that the result of the speech recognition is displayed to display means. A method according to claim 2, wherein a control is performed so that the result of the speech recognition is printed by printing means. A method according to claim 1, wherein a control is performed so that the speech is inputted by a microphone. A method according to claim 1, wherein a control is performed so that the speech is inputted from a terminal connected through a communication line and the result of the speech recognition is outputted to the terminal connected through the communication line. A speech recognizing apparatus comprising: input means for inputting a speech including a speech portion and a non-speech portion; speech portion Cepstrum long time mean deriving means (6) for deriving a Cepstrum long time mean of the speech portion from the speech portion included in the input speech; non-speech portion Cepstrum long time mean deriving means (8) for deriving a Cepstrum long time mean of the non-speech portion from the non-speech portion included in the input speech; linear spectrum processing means for subtracting a Cepstrum long time mean of the non-speech portion from the Cepstrum long time mean of the speech portion on a linear spectrum dimension to obtain a first substracted result converting means (11) for converting the first subtracted result into a Cepstrum dimension; and speech model deriving means for subtracting a Cepstrum long time mean of a speech portion in a speech database for learning from the converted result to obtain a second subtracted result, and adding the second subtracted result to a speech model expressed by a Cepstrum to obtain a modified model. An apparatus according to claim 11, further having speech recognizing means for recognizing the input speech by using the modified speech model obtained by the speech model deriving means. An apparatus according to claim 11, wherein the Cepstrum long time mean is data of a hidden Markov model. An apparatus according to claim 11, further comprising: noise multiplexed model forming means for adding and synthesizing a noise model that is expressed by Cepstrum parameters and the speech model expressed by the Cepstrum parameters on a linear spectrum dimension, thereby forming a noise multiplexed model, and wherein the linear spectrum processing means uses a mean value parameter of the noise model as a Cepstrum parameter long time mean of the non-speech portion. An apparatus according to claim 11, further comprising memory means in which a control program for each of the processes has been stored. An apparatus according to claim 12, wherein the speech recognizing means performs a speech recognition by a hidden Markov model. An apparatus according to claim 12, further comprising display means for displaying the result of the speech recognition by the speech recognizing means. An apparatus according to claim 12, further comprising printing means for printing the result of the speech recognition by the speech recognizing means. An apparatus according to claim 11, further comprising a microphone for inputting the speech. An apparatus according to claim 11, further comprising communication control means for controlling so as to input the speech from a terminal connected through a communication line and output the result of the speech recognition to the terminal connected through the communication line. A signal product conveying instructions for causing a programmable processing apparatus to perform a method according to any one of claims 1 to 10 when said signal product is loaded into said programmable processing apparatus. A signal product according to claim 21 when recorded on a storage device. A computer usable medium carrying computer usable instructions for causing a programmable processing apparatus to carry out a speech recognition method according to any one of claims 1 to 10 when said medium is loaded into said programmable processing apparatus.

Inventors: KOSAKA TETSUO, OHORA YASUNORI, KOSAKA, TETSUO, OHORA, YASUNORI
Applicants: CANON KK, CANON KABUSHIKI KAISHA
Application year: 1997
Main classification: G10L15
IPC codes: G10L15