Data processing: speech signal processing – linguistics – language – Speech signal processing – Recognition
Reexamination Certificate
2000-07-18
2003-12-30
To, Doris H. (Department: 2655)
Data processing: speech signal processing, linguistics, language
Speech signal processing
Recognition
C704S256000
Reexamination Certificate
active
06671669
ABSTRACT:
BACKGROUND OF THE INVENTION
I. Field of the Invention
The present invention pertains generally to the field of communications and more specifically to a novel and improved system and method for voice recognition.
II. Description of the Related Art
Voice recognition (VR) represents one of the most important techniques to endow a machine with simulated intelligence to recognize user or user-voiced commands and to facilitate human interface with the machine. VR also represents a key technique for human speech understanding. Systems that employ techniques to recover a linguistic message from an acoustic speech signal are called voice recognizers. The term “voice recognizer” is used herein to mean generally any spoken-user-interface-enabled device. A voice recognizer typically comprises an acoustic processor a feature extractor and a word decoder. The acoustic processor extracts a sequence of information-bearing features, or vectors, necessary to achieve VR of the incoming raw speech. The word decoder decodes the sequence of features, or vectors, to yield a meaningful and desired output format such as a sequence of linguistic words corresponding to the input utterance.
The acoustic processor represents a front-end speech analysis subsystem in a voice recognizer. In response to an input speech signal, the acoustic processor provides an appropriate representation to characterize the time-varying speech signal. The acoustic processor should discard irrelevant information such as background noise, channel distortion, speaker characteristics, and manner of speaking. Efficient acoustic processing furnishes voice recognizers with enhanced acoustic discrimination power. To this end, a useful characteristic to be analyzed is the short time spectral envelope. Two commonly used spectral analysis techniques for characterizing the short time spectral envelope are linear predictive coding (LPC) and filter-bank-based spectral modeling. Exemplary LPC techniques are described in U.S. Pat. No. 5,414,796, entitled VARIABLE RATE VOCODER, which is assigned to the assignee of the present invention and fully incorporated herein by reference, and L. B. Rabiner & R. W. Schafer, Digital Processing of Speech Signals 396-453 (1978), which is also fully incorporated herein by reference.
The use of VR (also commonly referred to as speech recognition) is becoming increasingly important for safety reasons. For example, VR may be used to replace the manual task of pushing buttons on a wireless telephone keypad. This is especially important when a user is initiating a telephone call while driving a car. When using a phone without VR, the driver must remove one hand from the steering wheel and look at the phone keypad while pushing the buttons to dial the call. These acts increase the likelihood of a car accident. A speech-enabled phone (i.e., a phone designed for speech recognition) would allow the driver to place telephone calls while continuously watching the road. And a hands-free car-kit system would additionally permit the driver to maintain both hands on the steering wheel during call initiation.
Speech recognition devices are classified as either speaker-dependent (SD) or speaker-independent (SI) devices. Speaker-dependent devices, which are more common, are trained to recognize commands from particular users. In contrast, speaker-independent devices are capable of accepting voice commands from any user. To increase the performance of a given VR system, whether speaker-dependent or speaker-independent, training is required to equip the system with valid parameters. In other words, the system needs to learn before it can function optimally.
A speaker-dependent VR device typically operates in two phases, a training phase and a recognition phase. In the training phase, the VR system prompts the user to speak each of the words in the system's vocabulary once or twice (typically twice) so the system can learn the characteristics of the user's speech for these particular words or phrases. An exemplary vocabulary for a hands-free car kit might include the digits on the keypad; the keywords “call,” “send,” “dial,” “cancel,” “clear,” “add,” “delete,” “history,” “program,” “yes,” and “no”; and the names of a predefined number of commonly called coworkers, friends, or family members. Once training is complete, the user can initiate calls in the recognition phase by speaking the trained keywords, which the VR device recognizes by comparing the spoken utterances with the previously trained utterances (stored as templates) and taking the best match. For example, if the name “John” were one of the trained names, the user could initiate a call to John by saying the phrase “Call John.” The VR system would recognize the words “Call” and “John,” and would dial the number that the user had previously entered as John's telephone number. Systems and methods for training
A speaker-independent VR device also uses a training template that contains a prerecorded vocabulary of a predefined size (e.g., certain control words, the numbers zero through nine, and yes and no). A large number of speakers (e.g., 100) must be recorded saying each word in the vocabulary. An example of a speaker-independent VR is the Dynamic Time Warping (DTW) engine described in U.S. Patent Application Ser. No. 09/615,572 entitled METHOD AND APPARATUS FOR CONSTRUCTING VOICE TEMPLATES FOR A SPEAKER-INDEPENDENT VOICE RECOGNITION SYTEM, filed Jul. 13, 2000, which is assigned to the assignee of the present invention and fully incorporated herein by reference.
Different speaker independent VRs may yield different results. For example, a speaker independent (SI) Hidden Markov Model (HMM) engine may yield a different result than a speaker independent Dynamic Time Warping (DTW) engine. Combining the results of both these engines can result in a system with better recognition accuracy and lower rejection rates than using the results of only one of the engines.
A speaker-dependent VR and a speaker independent VR may yield different results. A speaker dependent engine performs recognition using templates pertaining to a specific user. A speaker independent engine performs recognition using templates generated using exemplars from an ensemble of users. Since speaker specific templates are closer to a given user's speaking style, SD engines provide better accuracy than SI engines. However, SI engines have the advantage that the users need not go through the “training process” prior to using the system.
A system and method that combines engines of different types is desired. Combining a speaker dependent VR with a speaker independent VR would provide enhanced accuracy and use a greater amount of information in the input speech signal. Thus, a system and method for resolving different results from a speaker-dependent VR and a speaker independent VR is desired.
SUMMARY OF THE INVENTION
The described embodiments are directed to a system and method for voice recognition that uses combined voice recognition engines. In one aspect, an acoustic processor is configured to extract speech parameters from digitized speech samples of an utterance; a plurality of voice recognition engines are coupled to the acoustic processor, each voice recognition engine producing a hypothesis; and decision logic takes as input the hypotheses from the voice recognition engines and selects a hypothesis.
In another aspect, a method of combining a plurality of voice recognition engines to improve voice recognition is provided. The method advantageously includes extracting speech parameters by an acoustic processor from digitized speech samples of an utterance; coupling a plurality of voice recognition engines to the acoustic processor; producing a hypothesis from each voice recognition engine; and selecting a hypothesis from the hypotheses produced by the plurality of voice recognition engines.
In one embodiment, speaker-independent voice recognition engines are combined. In another embodiment, speaker-dependent voice recognition engines are combined. In yet another embodiment, a spea
Bi Ning
Garudadri Harinath
Oses David Puig
Qi Yingyong
Brown Charles D.
Opsasnick Michael N.
Pauley Nicholas J.
Qualcomm Incorporated
To Doris H.
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