Conexant Systems, Inc.

U S West, Inc.

Mediaone Group, Inc.

Aruna Bayya

Marvin L Vis

Dianne L Steiger

In a speech training and recognition system, the current invention detects and warns the user about the similar sounding entries to vocabulary and permits entry of such confusingly similar terms which are marked along with the stored similar terms to identify the similar words. In addition, the states in similar words are weighted to apply more emphasis to the differences between similar words than the similarities of such words. Another aspect of the current invention is to use modified scoring algorithm to improve the recognition performance in the case where confusing entries were made to the vocabulary despite the warning. Yet another aspect of the current invention is to detect and warn the user about potential problems with new entries such as short words and two or more word entries with long silence periods in between words. Finally, the current invention also includes alerting the user about the dissimilarity of the multiple tokens of the same vocabulary item in the case of multiple-token training.

Smart training and smart scoring in SD speech recognition system with user defined vocabulary

A speech recognition training system that provides for model generation to be used within speaker dependent speech recognition systems requiring very limited training data, including single token training. The present invention provides a very fast and reliable training method based on the segmentation of a speech signal for subsequent estimating of speaker dependent word models. In addition, the invention provides for a robust method of performing end-point detection of a word contained within a speech utterance or speech signal. The invention is geared ideally for speaker dependent speech recognition systems that employ word-based speaker dependent models. The invention provides the end-point detection method is operable to extract a desired word or phrase from a speech signal that is recorded in varying degrees of undesirable background noise. In addition, the invention provides a simplified method of building the speaker dependent models using a simplified hidden Markov modeling method. The invention requires very limited training and is operable within systems having constrained budgets of memory and processing resources. Some examples of candidate application areas include cellular telephones and other devices that would benefit from abbreviated training and have inherently limited memory and processing power.

Speaker dependent speech recognition training using simplified hidden markov modeling and robust end-point detection

A speaker-dependent (SD) speech recognition system. The invention is specifically tailored to operate with very little training data, and also within hardware constraints such as limited memory and processing resources. A garbage model and a vocabulary model are generated and are subsequently used to perform comparison to a speech signal to decide if the speech signal is a specific vocabulary word. A word score is generated, and it is compared to a number of parameters, including an absolute threshold and another word score. Off-line training of the system is performed, in one embodiment, using compressed training tokens. A speech signal is segmented into scramble frames wherein the scramble frames have certain characteristics. For example, length is one characteristic of the scramble frames, each scramble frame having a length of an average vowel sound, or a predetermined length of nominally 40-50 msec. The invention is operable to be trained using as little as one single training token that is segmented. Those segments may be re-arranged to form a pseudo-token to form a garbage model. The use of a pseudo-token allows for generation of a reliable garbage model having many speaker-specific characteristics of an original training token while discarding the specific acoustic characteristics of any vocabulary word corresponding to the training token. The invention is equally as operable by using a training token to form a vocabulary model having multiple states, and re-arranging those states to form one or more garbage models.

Method of independently creating and using a garbage model for improved rejection in a limited-training speaker-dependent speech recognition system

A speech signal divided into frames, each frame having a sound type, and a class is determined for each frame depending on the sound type of the frame. One of multiple filters is selected for each frame depending on the class of the frame. Each frame is filtered according to the filter selected, and the filtered frames combined to provide a filtered speech signal. The system includes filters and software.

Method and system for region based filtering of speech

A method and system for adaptively filtering a speech signal. The method includes decomposing the signal into subbands, which may include performing a discrete Fourier transform on the signal to provide approximately orthogonal components. The method also includes determining a speech quality indicator for each subband, which may include estimating a signal-to-noise ratio for each subband. The method also includes selecting a filter for filtering each subband depending on the speech quality indicator, which may include estimating parameters for the filter based on a clean speech signal. The method further includes determining an overall average error for the filtered subbands, which may include calculating a mean-squared error. The method still further includes identifying at least one filtered subband which, if excluded from the filtered speech signal, would reduce the overall average error determined, and combining, with exception of the filtered subbands identified, the filtered subbands to provide an estimated filtered speech signal. The system includes filters and software for performing the method.

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Irvine, CA, United States of America

Aruna Bayya