Abstract: A device receives a signal that includes human-interpretable audio information. The device automatically adjusts the volume of the audio information at the received end. The volume control is determined by an automatic volume control gain, which is calculated as a function of the automatic gain control gain, a weighted dynamic range compression gain, and a weighted constant gain.

Abstract: A compressed pronunciation lexicon file is generated from a source pronunciation lexicon using a pronunciation prediction algorithm in a multi-output mode. The pronunciation prediction algorithm may generate a deterministic ordered list of phoneme strings from the textual representation of a particular word. The compressed pronunciation lexicon file may include a sorted list of records of compressed textual representations of words and compressed phonetic representations of the words.

Abstract: When a speaker-independent voice-recognition (SIVR) system recognizes a spoken utterance that matches a phonetic representation of a speech element belonging to a predefined vocabulary, it may play a synthesized speech fragment as a means for the user to verify that the utterance was correctly recognized. When a speech element in the vocabulary has more than one possible pronunciation, the system may select the one most closely matching the user's utterance, and play a synthesized speech fragment corresponding to that particular representation.

Abstract: Based on an automatic gain factor derived from a long term peak value sample is a frame of a received signal containing speech are adjusted to enhance intelligibility. A dynamic range compression algorithm may also be used to further enhance intelligibility.

Abstract: A device receives a signal that includes human-interpretable audio information. The device detects sound locally and analyzes it to determine if an intermittent component is present. If the intermittent component is present, the received signal is altered so that the audio information is more easily human-interpretable when the signal is performed. The device can be a portable telephone. The intermittent component can be detected, for example, in music.

Abstract: A device receives a signal that includes human-interpretable audio information. The device detects sound locally and analyzes it to determine if an intermittent component is present. If the intermittent component is present, the received signal is altered so that the audio information is more easily human-interpretable when the signal is performed. The device can be a portable telephone. The intermittent component can be detected, for example, in music.

Abstract: A speech recognition system includes a token builder, a noise estimator, a template padder, a gain and noise adapter and a dynamic time warping (DTW) unit. The token builder produces a widened test token representing an input test utterance and at least one frame before and after the input test utterance. The noise estimator estimates noise qualities of the widened test token. The template padder pads each of a plurality of reference templates with at least one blank frame either the beginning or end of the reference template. The gain and noise adapter adapts each padded reference template with the noise and gain qualities thereby producing adapted reference templates having noise frames wherever a blank frame was originally placed and noise adapted speech where speech exists. The DTW unit performs a noise adapted DTW operation comparing the widened token with one of the noise adapted reference templates, wherein, when comparing against one of the noise frames, no duration constraints are used.

Abstract: A keyword recognition system for speaker dependent, dynamic time warping (DTW) recognition systems uses all of the trained word templates in the system, (keyword and vocabulary), to determine if an utterance is a keyword utterance or not. The utterance is selected as the keyword if a keyword score indicates a significant match to the keyword template and if the keyword score indicates a better match than do the entirety of scores to the vocabulary word templates.

Abstract: A device receives a signal that includes human-interpretable audio information. The device detects sound locally and analyzes it to determine if an intermittent component is present. If the intermittent component is present, the received signal is altered so that the audio information is more easily human-interpretable when the signal is performed. The device can be a portable telephone. The intermittent component can be detected, for example, in music.

Abstract: A device receives a signal that includes human-interpretable audio information. The device automatically adjusts the volume of the audio information at the received end. The volume control is determined by an automatic volume control gain, which is calculated as a function of the automatic gain control gain, a weighted dynamic range compression gain, and a weighted constant gain.

Abstract: A speech recognition system includes a token builder, a noise estimator, a template padder, a gain and noise adapter and a dynamic time warping (DTW) unit. The token builder produces a widened test token representing an input test utterance and at least one frame before and after the input test utterance. The noise estimator estimates noise qualities of the widened test token. The template padder pads each of a plurality of reference templates with at least one blank frame either the beginning or end of the reference template. The gain and noise adapter adapts each padded reference template with the noise and gain qualities thereby producing adapted reference templates having noise frames wherever a blank frame was originally placed and noise adapted speech where speech exists. The DTW unit performs a noise adapted DTW operation comparing the widened token with one of the noise adapted reference templates, wherein, when comparing against one of the noise frames, no duration constraints are used.

Abstract: A speech recognition system includes a token builder, a noise estimator, a template padder, a gain and noise adapter and a dynamic time warping (DTW) unit. The token builder produces a widened test token representing an input test utterance and at least one frame before and after the input test utterance. The noise estimator estimates noise qualities of the widened test token. The template padder pads each of a plurality of reference templates with at least one blank frame either the beginning or end of the reference template. The gain and noise adapter adapts each padded reference template with the noise and gain qualities thereby producing adapted reference templates having noise frames wherever a blank frame was originally placed and noise adapted speech where speech exists. The DTW unit performs a noise adapted DTW operation comparing the widened token with one of the noise adapted reference templates.

Abstract: A speech recognition system includes a token builder, a noise estimator, a template padder, a gain and noise adapter and a dynamic time warping (DTW) unit. The token builder produces a widened test token representing an input test utterance and at least one frame before and after the input test utterance. The noise estimator estimates noise qualities of the widened test token. The template padder pads each of a plurality of reference templates with at least one blank frame either the beginning or end of the reference template. The gain and noise adapter adapts each padded reference template with the noise and gain qualities thereby producing adapted reference templates having noise frames wherever a blank frame was originally placed and noise adapted speech where speech exists. The DTW unit performs a noise adapted DTW operation comparing the widened token with one of the noise adapted reference templates, wherein, when comparing against one of the noise frames, no duration constraints are used.

Abstract: Based on an automatic gain factor derived from a long term peak value sample is a frame of a received signal containing speech are adjusted to enhance intelligibility. A dynamic range compression algorithm may also be used to further enhance intelligibility.

Abstract: A keyword recognition system for speaker dependent, dynamic time warping (DTW) recognition systems uses all of the trained word templates in the system, (keyword and vocabulary), to determine if an utterance is a keyword utterance or not. The utterance is selected as the keyword if a keyword score indicates a significant match to the keyword template and if the keyword score indicates a better match than do the entirety of scores to the vocabulary word templates.

Abstract: A keyword recognition system for speaker dependent, dynamic time warping (DTW) recognition systems uses all of the trained word templates in the system, (keyword and vocabulary), to determine if an utterance is a keyword utterance or not. The utterance is selected as the keyword if a keyword score indicates a significant match to the keyword template and if the keyword score indicates a better match than do the entirety of scores to the vocabulary word templates.

Abstract: A pattern recognition system and method is disclosed. The method includes the steps of a) providing a noisy test feature set of the input signal, a plurality of reference feature sets of reference templates produced in a quiet environment, and a background noise feature set of background noise present in the input signal, b) producing adapted reference templates from the test feature set, the background noise feature set and the reference feature sets and c) determining match scores defining the match between each of the adapted reference templates and the test feature set. The method can also include adapting the scores before accepting a score as the result. The system and method are described for both Hidden Markov Model (HMM) and Dynamic Time Warping (DTW) scoring units. The system performs the steps of the method.

Abstract: A method is disclosed for use in preprocessing noisy speech to minimize likelihood of error in estimation for use in a recognizer. The computationally-feasible technique, herein called Minimum-Mean-Log-Spectral-Distance (MMLSD) estimation using mixture models and Marlov models, comprises the steps of calculating for each vector of speech in the presence of noise corresponding to a single time frame, an estimate of clean speech, where the basic assumptions of the method of the estimator are that the probability distribution of clean speech can be modeled by a mixture of components each representing a different speech class assuming different frequency channels are uncorrelated within each class and that noise at different frequency channels is uncorrelated.