Data processing: speech signal processing – linguistics – language – Speech signal processing – Recognition
Reexamination Certificate
1999-02-08
2003-06-03
Chawan, Vijay (Department: 2654)
Data processing: speech signal processing, linguistics, language
Speech signal processing
Recognition
C704S252000, C704S238000, C704S240000, C704S243000, C704S246000, C704S254000
Reexamination Certificate
active
06574596
ABSTRACT:
BACKGROUND OF THE INVENTION
I. Field of the Invention
The present invention pertains generally to the field of communications, and more specifically to voice recognition systems.
II. Background
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. A voice recognizer typically comprises an acoustic processor, which extracts a sequence of information-bearing features, or vectors, necessary to achieve VR of the incoming raw speech, and a word decoder, which 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. To increase the performance of a given system, training is required to equip the system with valid parameters. In other words, the system needs to learn before it can function optimally.
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, 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 or speaker-independent devices. Speaker-independent devices are capable of accepting voice commands from any user. Speaker-dependent devices, which are more common, are trained to recognize commands from particular users. 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 so the system can learn the characteristics of the user's speech for these particular words or phrases. Alternatively, for a phonetic VR device, training is accomplished by reading one or more brief articles specifically scripted to cover all of the phonemes in the language. 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. 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.
The throughput of a VR system may be defined as the percentage of instances that a user goes through a recognition task successfully. A recognition task typically comprises multiple steps. For example, in voice dialing with a wireless telephone, the throughput refers to the average percentage of times that a user completes a telephone call successfully with the VR system. The number of steps necessary to achieve a successful telephone call with VR can vary from one call to another. In general, the throughput of a VR system depends mainly on two factors, the recognition accuracy of the VR system, and the human-machine interface. A human user's subjective perception of VR system performance is based on throughput. Thus, there is a need for a VR system with high recognition accuracy and an intelligent human-machine interface to increase throughput.
SUMMARY OF THE INVENTION
The present invention is directed to a VR system with high recognition accuracy and an intelligent human-machine interface to increase throughput. Accordingly, in one aspect of the invention, a method of capturing an utterance in a voice recognition system advantageously includes the steps of accepting the utterance if a first predefined relationship exists between at least one comparison result for the utterance with respect to a stored word and at least one difference between the at least one comparison result and at least one other comparison result between the utterance and at least one other stored word; applying an N-best algorithm to the utterance if a second predefined relationship exists between the at least one comparison result and the at least one difference between the at least one comparison result and the at least one other comparison result; and rejecting the utterance if a third predefined relationship exists between the at least one comparison result and the at least one difference between the at least one comparison result and the at least one other comparison result.
In another aspect of the invention, a voice recognition system advantageously includes an acoustic processor configured to extract speech parameters from digitized speech samples of an utterance; and a processor coupled to the acoustic processor and configured to (1) accept the utterance if a first predefined relationship exists between at least one comparison result for the utterance with respect to a stored word and at least one difference between the at least one comparison result and at least one other comparison result between the utterance and at least one other stored word, (2) apply an N-best algorithm to the utterance if a second predefined relationship exists between the at least one comparison result and the at least one difference between the at least one comparison result and the at least one other comparison result, or (3) reject the utterance if a third predefined relationship exists between the at least one comparison result and the at least one difference between the at least one comparison result and the at least one other comparison result.
In another aspect of the invention, a voice recognition system advantageously includes means for accepting an utterance if a first predefined relationship exists between at least one comparison result for the utterance with respect to a stored word and at least one difference between the at least one comparison result and at least one other comparison result between the utterance and at least one other stored word; means for applying an N-best algorithm to the utterance if a s
Bi Ning
Chang Chienchung
Dejaco Andrew P.
Garudadri Harinath
Brown Charles D.
Chawan Vijay
Pappas George C.
Qualcomm Incorporated
Wadsworth Philip R.
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