Speech analysis method and apparatus
First Claim
1. A method of speech analysis comprising:
- receiving a speech signal;
converting the speech signal into a converted signal for processing by a processor and memory system;
generating a feature vector from said converted signal, said feature vector having a plurality of feature vector elements;
providing a reference model comprising a plurality of states, each of said states comprising an associated means vector and covariance matrix;
generating an error vector having a plurality of error elements, each of said error elements corresponding to one of said feature vector elements;
weighting each of said error elements by a respective weight factor raised by an exponent, each respective weight factor comprising a factor proportional to a relative variance of each of said feature vector elements;
generating an observation score based on said feature vector, said weighted error elements, and said reference model states; and
based on a series of said observation scores, determining the probability that received speech signals correspond to a particular series of said reference model states.
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Abstract
System (100) receives a speech signal at an input (102) which is measured and transformed by speech feature measuring device (104). The output feature vector from speech feature measuring device (104) is then compared to a reference model in a statistical classification manner. Acoustic similarity measuring device (106) performs statistical measurements while temporal speech model constraints block (108) imposes transitional probabilities to the probability measurements generated by measuring device (106). Acoustic similarity measuring device (106) performs a weighted analysis of the error vector defined between the speech feature vector and reference vector utilized during the analysis.
45 Citations
11 Claims
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1. A method of speech analysis comprising:
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receiving a speech signal; converting the speech signal into a converted signal for processing by a processor and memory system; generating a feature vector from said converted signal, said feature vector having a plurality of feature vector elements; providing a reference model comprising a plurality of states, each of said states comprising an associated means vector and covariance matrix; generating an error vector having a plurality of error elements, each of said error elements corresponding to one of said feature vector elements; weighting each of said error elements by a respective weight factor raised by an exponent, each respective weight factor comprising a factor proportional to a relative variance of each of said feature vector elements; generating an observation score based on said feature vector, said weighted error elements, and said reference model states; and based on a series of said observation scores, determining the probability that received speech signals correspond to a particular series of said reference model states. - View Dependent Claims (2, 3, 4, 5, 6)
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7. An apparatus for performing speech analysis, comprising:
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circuitry for receiving a speech signal; circuitry for converting said speech signal to a converted signal for processing by a processor and memory system; circuitry for transmitting said converted signal to speech feature measuring circuitry; said speech feature measuring circuitry for generating a feature vector from said converted signal, said feature vector having a plurality of feature vector elements; a memory for storing a reference model comprising a plurality of states, each of said states comprising an associated mean vector and covariance matrix; acoustic similarity measuring circuitry, including; circuitry for generating an error vector having a plurality of error elements, each of said error elements corresponding to one of said feature vector elements; circuitry for weighting each of said error elements of the error vector by a respective weight factor raised by an exponent, each respective weight factor comprising a factor proportional to a relative variance of each of said feature vector elements; circuitry for generating an observation score based on said feature vector, said weighted error elements, and said reference model states; and circuitry for determining, based on a series of said observation scores, the probability that received speech signals correspond to a particular series of said reference model states. - View Dependent Claims (8, 9, 10, 11)
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Specification