Method for robust voice recognition by analyzing redundant features of source signal

A method for processing digitized speech signals by analyzing redundant features to provide more robust voice recognition. A primary transformation is applied to a source speech signal to extract primary features therefrom. Each of at least one secondary transformation is applied to the source speech signal or extracted primary features to yield at least one set of secondary features statistically dependant on the primary features. At least one predetermined function is then applied to combine the primary features with the secondary features. A recognition answer is generated by pattern matching this combination against predetermined voice recognition templates.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to voice recognition techniques and circuits, and more particularly, to a system for more accurate and noise-tolerant robust voice recognition by analyzing redundant features of a source signal.

2. Description of the Related Art

Various signal processing techniques have been developed for analyzing digitized speech signals in order to recognize the underlying content of such speech. Once recognized, this content can then be used to control a handheld telephone, computer, household appliance, or other device. Some such known techniques employ short-time Fourier spectra or “spectrogram” of a speech signal, which are computed using windowed Fourier transforms as explained more fully in Rabiner et al.,Fundamentals of Speech Recognition, the entirety of which is incorporated herein by reference.

FIG. 1shows one known spectral feature extractor100for spectral analysis, which includes stages of windowing102, FFT104, MEL/BARK filtering106, Log108, and RASTA filtering110. A digitized input speech signal101is fed into the windowing stage102, which divides the input signal into smaller sized segments of appropriate duration, such as 20 milliseconds. The FFT stage104performs a Fast Fourier Transform to windowed segments output by the stage102. The MEL/BARK stage106performs warping of the linear frequency scale to a different scale, so that the resolution for lower frequencies is greater than that for higher frequencies. The resolution on the frequency scale becomes progressively coarser from low frequencies to high frequencies in the hearing range. MEL scale and BARK scale are two known transformations that result in the above frequency warping. These two (and some variations) are commonly used in speech recognition. The Log stage108takes the Logarithm of the input number, and more particularly, the log of each MEL/BARK transformed spectral value that has been computed. The foregoing stages102-108are described in various known publications, with one example being the above-cited text Rabiner et al.,Fundamentals of Speech Recognition.

The RASTA stage110serves to filter the output of the Log stage108by a predefined bandpass filter. For example, if there are sixteen BARK numbers, there will be sixteen filters operating on each of the bark bands. The RASTA stage110may be implemented by any known RASTA processing technique, with one example being described in U.S. Pat. No. 5,450,522 entitled “Auditory Model for Parameterization of Speech” to Hermansky et al., the entirety of which is incorporated herein.

The output of the spectral feature extractor100comprises spectral output signals111, which are thereafter processed by various subsequent techniques (not shown) to yield a “recognition answer” that gives the predicted content of the input speech signal. Recognition answers based on such spectral output signals111provide decent accuracy in low noise environments. Advantageously, degradation of their accuracy occurs slowly with decreasing signal-to-noise ratios. Spectral output signals can be further processed in various ways. For instance, one approach further processes the spectral output signals111by a cepstral transformation112to yield cepstral output signals114. One type of cepstral transformation112, for example, utilizes a discrete cosine transform (DCT) followed by a dimensionality reduction. Broadly, “cepstrum” is explained as the inverse Fourier transform of the logarithm of the power spectrum of a signal, as further discussed in the following references, hereby incorporated by reference in their entirety: A. V. Oppenheim and R. W. Schafer,Discrete-Time Signal Processing, J. R. Deller, Jr., J. G. Proakis and J. H. L. Hansen,Discrete-Time Processing of Speech Signals, and L. R. Rabiner and R. W. Schafer,Digital Processing of Speech Signals.

In systems where the cepstrum114is calculated, the cepstrum (rather than the spectrum111) is processed by statistical modeling techniques to yield a recognition answer. One benefit of basing recognition answers upon cepstral output signals114is that they provide more accurate voice recognition at low levels of noise. However, as noise increases, the error rate increases rapidly for these systems. Therefore, neither spectral nor cepstral voice recognition systems are entirely adequate for applications that could potentially encounter a wide range of noise levels.

SUMMARY OF THE INVENTION

Broadly, the present invention concerns a method for processing digitized speech signals for voice recognition. Unlike conventional approaches, which seek compactness and simplicity of operation by removing redundant features of input speech prior to recognition, the present invention purposefully retains and analyzes redundant features of a source signal in order to perform voice recognition accurately in a variety of acoustic environments. A primary transformation is applied to a digitized source speech signal to extract primary features therefrom. One example is a spectral transformation applied to extract spectral features. Each of at least one secondary transformation is applied to the source speech signal or extracted spectral features to yield at least one set of secondary features. Each secondary transformation is designed to yield data containing some information that is already present in the extracted primary features. At least one predetermined function is then utilized to combine the primary features with the secondary features. A recognition answer is generated by pattern matching this combination against a predetermined set of voice recognition templates.

The invention affords its users with a number of distinct advantages. Chiefly, the invention provides accurate voice recognition with increasing levels of noise without sacrificing performance in low noise environments. This differs from conventional cepstral voice recognition systems, where performance rapidly drops with increasing noise, and also differs from conventional spectral voice recognition systems, where performance degrades more slowly with increasing noise with the tradeoff of some performance in low noise environments. The invention also provides a number of other advantages and benefits, which should be apparent from the following description of the invention.

DETAILED DESCRIPTION

The nature, objectives, and advantages of the invention will become more apparent to those skilled in the art after considering the following detailed description in connection with the accompanying drawings.

Hardware Components & Interconnections

Overall Structure

One aspect of the invention concerns a voice recognition system, which may be embodied by various hardware components and interconnections, with one example being described by the voice recognition system200as shown in FIG.2. The system200serves to process an input speech signal201, which comprises a digitized speech signal. Optionally, the system200may include a digital-to-analog converter (DAC) to digitize analog input speech signals. The input speech signal201is fed to a spectral feature extractor202, which comprises circuitry for extracting spectral features from the input speech signal201. As one example, the extractor202may be embodied by components similar to the spectral feature extractor100shown in FIG.1.

Output202aof the extractor202comprises one or more spectral output signals, such as a spectral feature vector. The output202ais directed to a secondary transformation module204. The secondary transformation module204applies a predetermined function in order to provide a secondary output204a. In the illustrated example, the module204applies a cepstral transformation, in which case the secondary output204acomprises a set of cepstral vectors. The module204's secondary feature signals204aare directed to a feature combination module206. As an alternative to the foregoing embodiment, where the secondary transformation module204receives output202aof the extractor202, the module204may instead receive the original input speech signal201. In still another embodiment, there may be multiple secondary transformation modules204, each applying a different function to the input speech signal201or spectral output202a.

In any case, the foregoing description of spectral and cepstral features is merely exemplary, and the scope of the present disclosure nonetheless contemplates a variety of different primary and secondary transformations (not necessarily spectrum and cepstrum). As an example, the spectral feature extractor202and its spectral features202amay be represented by any appropriate primary feature extractor202performing a first or higher order transformation to create a time-frequency representation of the input speech signal. Some examples include a spectral transformation, wavelet transformation, modulation spectrum, cumulants, etc.

Each secondary transformation may be implemented by a discrete cosine transform (producing cepstrum), principal component transform, or other projection of the primary features into another known or user-defined space. The secondary features may be produced mathematically, empirically, or by another means. In any case, each set of secondary features is “statistically dependent” upon the primary features, meaning that the secondary features are related to the primary features by mathematical function. In other words, it is possible to derive the secondary features by applying a mathematical function to the primary features. The secondary features, for instance, may be related from the primary features by correlation (including but not limited to addition, subtraction, multiplication by coefficients and adding, or another combination), nonlinear processing, or another technique. The primary features may, or may not, be mathematically reproducible from the secondary features. As one example, then, ceptstral features of the module204are statistically dependent on the spectral features of the extractor202, and in fact, the illustrated cepstral vectors are derived by mathematically transforming the spectral vectors using cepstrum. Hence, outputs202a,204acontain redundant information regarding the input speech signal201.

Unlike conventional voice recognition systems, where spectral or other primary output signals are not used except for further downstream (serial) processing, the spectral output signals202aare separately analyzed apart from their use in the secondary transformation204. Namely, the output signals202aare fed directly to the feature combination module206for analysis.

The module206combines the spectral output signals202aand the secondary output signals204a. This combination may occur in various ways. As one example, the signals202a,204amay be combined by concatenation, linear discriminate analysis (LDA), principal component transform (PCT), or another function applied to both outputs202a,204atogether. In a different embodiment, the module206may include processing subcomponents206a,206bthat separately modify one or both of the signals202a,204aafter which the module206combines the signals202a,204aas modified. If more than one secondary transformation204is performed, the module206may include an equal number of components206b. Each subcomponent206a,206bmay perform various functions, such as scaling (i.e., multiplying) its input signal by a fixed or variable coefficient, changing the exponent of the input signal, multiplying the input signal by itself one or more times (self-multiplying), or another one or more linear or nonlinear processes. In this embodiment, the modified signals are then combined using one of the foregoing techniques, e.g., concatenation, LDA, PCT, etc.

As mentioned above, there may be multiple secondary transformation modules204, each of which receives an input signal such as the input signal201or the output202aof the extractor202. In such embodiment, the feature combination module206still operates to combine spectral feature signals with secondary transformation output signals, although the secondary transformation output signals come from multiple different modules204.

The feature combination module206feeds its output to a statistical modeling engine208, also referred to as a “recognizer” or a “pattern matching” unit. The engine208, which may be implemented by a number of known techniques, produces an output comprising a recognition answer210. The recognition answer210constitutes the system200's estimation of the meaning of the input speech signal201. The engine208includes a training input208afor receiving input that trains the engine208to recognize certain model or sample speech patterns. As one particular example, the engine208may cross-reference the output of the module206in a lookup table to obtain the represented meaning of the input speech signal201, i.e., its “answer.”

Exemplary Digital Data Processing Apparatus

As mentioned above, data processing entities such as the extractor202, secondary transformation module(s)204, feature combination module206, statistical modeling engine208, and the like may be implemented in various forms. As one example, each of these components (or two or more components collectively) may be implemented by a digital data processing apparatus, as exemplified by the hardware components and interconnections of the digital data processing apparatus300of FIG.3.

The apparatus300includes a processor302, such as a microprocessor, personal computer, workstation, or other processing machine, coupled to storage304. In the present example, the storage304includes a fast-access storage306, as well as nonvolatile storage308. One example of the fast-access storage306is random access memory (“RAM”), used to store the programming instructions executed by the processor302. The nonvolatile storage308may comprise, for example, battery backup RAM, EEPROM, one or more magnetic data storage disks such as a “hard drive”, a tape drive, or any other suitable storage device. The apparatus300also includes an input/output310, such as a line, bus, cable, electromagnetic link, or other means for the processor302to exchange data with other hardware external to the apparatus300.

Despite the specific foregoing description, ordinarily skilled artisans (having the benefit of this disclosure) will recognize that the apparatus discussed above may be implemented in a machine of different construction, without departing from the scope of the invention. As a specific example, one of the components306,308may be eliminated; furthermore, the storage304,306, and/or308may be provided on-board the processor302, or even provided externally to the apparatus300.

Logic Circuitry

In contrast to the digital data processing apparatus discussed above, a different embodiment of the invention uses logic circuitry instead of computer-executed instructions to implement one or all components of the system200. Depending upon the particular requirements of the application in the areas of speed, expense, tooling costs, and the like, this logic may be implemented by constructing an application-specific integrated circuit (“ASIC”) having thousands of tiny integrated transistors. Such an ASIC may be implemented with CMOS, TTL, VLSI, or another suitable construction. Other alternatives include a digital signal processing chip (“DSP”), discrete circuitry (such as resistors, capacitors, diodes, inductors, and transistors), field programmable gate array (“FPGA”), programmable logic array (“PLA”), and the like.

Wireless Telephone Application

In one exemplary application, the voice recognition system200may be implemented in a wireless telephone500(FIG.5), along with other circuitry known in the art of wireless telephony. The telephone500includes a speaker508, user interface510, microphone514, transceiver504, antenna506, and manager502. The manger502, which may be implemented by circuitry such as that discussed above in conjunction withFIGS. 3-4, manages operation and signal routing between the components504,508,510, and514. The manager502includes a voice recognition module502a, embodied by the system200, and serving to perform a function such a decoding speech commands of a human operator of the telephone500regarding dialing, call management, etc.

Operation

Having described the structural features of the present invention, the operational aspect of the present invention will now be described. Unlike conventional approaches, which seek compactness and simplicity of operation by removing redundant features of input speech prior to analysis, the present invention purposefully analyzes redundant features of a source signal in order to perform voice recognition accurately in a variety of acoustic environments.

Wherever the functionality of the invention is implemented using one or more machine-executed program sequences, these sequences may be embodied in various forms of signal-bearing media. In the context ofFIG. 3, such a signal-bearing media may comprise, for example, the storage304or another signal-bearing media, such as a magnetic data storage diskette400(FIG.4), directly or indirectly accessible by a processor302. Whether contained in the storage306, diskette400, or elsewhere, the instructions may be stored on a variety of machine-readable data storage media. Some examples include direct access storage (e.g., a conventional “hard drive”, redundant array of inexpensive disks (“RAID”), or another direct access storage device (“DASD”)), serial-access storage such as magnetic or optical tape, electronic non-volatile memory (e.g., ROM, EPROM, or EEPROM), battery backup RAM, optical storage (e.g., CD-ROM, WORM, DVD, digital optical tape), paper “punch” cards, or other suitable signal-bearing media including analog or digital transmission media and analog and communication links and wireless communications. In an illustrative embodiment of the invention, the machine-readable instructions may comprise software object code, compiled from a language such as assembly language, C, etc.

Logic Circuitry

In contrast to the signal-bearing medium discussed above, some or all of the invention's functionality may be implemented using logic circuitry, instead of using instruction processing machines. Such logic circuitry is therefore configured to perform operations to carry out the method of the invention. The logic circuitry may be implemented using many different types of circuitry, as discussed above.

Overall Sequence of Operation

FIG. 6shows a sequence600to illustrate an exemplary voice recognition process. Broadly, this sequence serves to process digitized speech signals by analyzing redundant features to provide more noise robust voice recognition. For ease of explanation, but without any intended limitation, the example ofFIG. 6is described in the context of the voice recognition system200described above.

The sequence is initiated in step602, where training of the system200is conducted. In the training step602, an input speech signal201(“training speech”) of desired characteristics is fed to the spectral feature extractor202, and steps604,606,608(discussed below) are performed utilizing this input speech signal. For instance, the training speech may comprise model speech (for a speaker independent system) or a particular person's sample speech (for a speaker dependent system). Output of the feature combination module206forms a voice recognition template, which is associated with the training speech by preparing a training input208a(signifying the content of the training speech), supplying the input208ato the statistical modeling engine208, and instructing the engine208to generate the desired recognition answer in the future whenever the system200encounters speech similar to the training speech. For instance, if the training speech signal201comprises the word “lesson,” then the text “lesson” is fed to the engine208in association with the training speech signal201. Further discussion of training is available from many sources, as many different techniques for training voice recognition systems are well known in the art. Some exemplary training schemes are discussed in the following materials, each incorporated herein by reference in its entirety: (1) U.S. patent application Ser. No. 09/248,513 entitled “Voice Recognition Rejection Scheme,” filed Feb. 8, 1999, (2) U.S. patent application Ser. No. 09/255,891 entitled “System and Method for Segmentation and Recognition of Speech Signals,” filed Jan. 4, 1999, and (3) U.S. patent application Ser. No. 09/615,572 entitled “Method and Apparatus for Constructing Voice Templates for a Speaker-independent Voice Recognition System,” filed Jul. 13, 2000. Additional training may be conducted at later times (not shown), as required or desired.

After some training602is concluded, a non-training input speech signal201is input to the spectral feature extractor202(step604). This signal201is that for which voice recognition is desired. Also in step604, the extractor202performs a spectral transformation upon the input speech signal201, which extracts spectral features from the input speech signal201and provides the extracted spectral features as the output202a. One exemplary spectral extraction technique includes linear predictive coding (LPC), which is described in U.S. Pat. No. 5,414,796, entitled “Variable Rate Encoder,” fully incorporated herein by reference, and the above-cited reference of Rabiner et al.,Digital Processing of Speech Signals.

In step606, the secondary transformation module204applies its secondary transformation to the spectral output202a. In the illustrated example, the module204applies a cepstral transformation to the output202a. Optionally, step606may also perform one or more additional secondary transformations (ceptstral or other types) in parallel with the illustrated transformation204. As mentioned above, all secondary transformations are statistically dependent to the spectral transformation of step604, meaning that that there is some common information carried by outputs202aand204b.

In step608, the module206combines the spectral output202awith the secondary output(s)204afrom the module204and any other modules of secondary transformation, if implemented. For ease of discussion, the present example is limited to the case where a single secondary transformation is used. As mentioned above, the combination of step608may occur in various ways. For instance, the module206may combine the signals202a,204aby concatenation, LDA, PCT, or another function. In a different embodiment, the processing subcomponents206a,206bfirst modify one or more of the signals202a,204aseparately, after which the module206combines the modified signals. Each subcomponent206a,206bmay perform a function such as scaling (i.e., multiplying) the input signal by a fixed or variable coefficient, changing the exponent of the input signal, multiplying the input signal by itself one or more times, or another one or more nonlinear or linear processes. In this embodiment, the signals as modified by206a,206bare then combined by one of the foregoing techniques, e.g., concatenation, LDA, PCT, etc.

In step610, the statistical modeling engine208generates a recognition answer, representing the estimated content of the input speech signal201. Step610is performed by pattern matching the signal from the module206against the predetermined set of voice recognition templates that were prepared in step602.

Other Embodiments

While the foregoing disclosure shows a number of illustrative embodiments of the invention, it will be apparent to those skilled in the art that various changes and modifications can be made herein without departing from the scope of the invention as defined by the appended claims. Furthermore, although elements of the invention may be described or claimed in the singular, the plural is contemplated unless limitation to the singular is explicitly stated. Additionally, ordinarily skilled artisans will recognize that operational sequences must be set forth in some specific order for the purpose of explanation and claiming, but the present invention contemplates various changes beyond such specific order.