US5459815A — Speech recognition method using time-frequency ma…

Claim 1

Independent Claim 123 words

A speech recognition method in which an input speech in converted to a time sequence of a feature vector, said feature vector including one of a spectrum and a cepstrum, and a distance or probability between the input speech time sequence and a time sequence of the feature vector or a statistical model thereof, is calculated for recognition, comprising the steps of: effecting a time frequency masking by an operation of obtaining a masked speech spectrum by subtracting, from speech spectrum at present, a masking pattern which is a function of frequency obtained by smoothing immediately preceding speech spectrum by time and frequency; and recognizing the speech by using the masked speech spectrum obtained by the above described operation at every time point.

Claim 2

Dependent Claim 123 words

A speech recognition method in which an input speech in converted to a time sequence of a feature vector, said feature vector including one of a spectrum and a cepstrum, and a distance or probability between the input speech time sequence and a time sequence of the feature vector or a statistical model thereof, is calculated for recognition, comprising the steps of: effecting a time frequency masking by an operation of obtaining a masked speech spectrum by subtracting, from speech spectrum at present, a masking pattern which is a function of frequency obtained by smoothing immediately preceding speech spectrum by time and frequency; and recognizing the speech by using the masked speech spectrum obtained by the above described operation at every time point.

Claim 3

Dependent Claim 58 words

A speech recognition method, comprising the steps of: converting an input speech to a digitized speech signal; converting said digitized speech signal to cepstrum coefficients at every prescribed time interval; obtaining a time sequence of dynamic cepstrum by subtracting a masking pattern from an input speech cepstrum at present; and recognizing the speech by using said dynamic cepstrum.

Claim 4

Dependent Claim 58 words

A speech recognition method, comprising the steps of: converting an input speech to a digitized speech signal; converting said digitized speech signal to cepstrum coefficients at every prescribed time interval; obtaining a time sequence of dynamic cepstrum by subtracting a masking pattern from an input speech cepstrum at present; and recognizing the speech by using said dynamic cepstrum.

Claim 5

Dependent Claim 50 words

The speech recognition method according to claim 2, wherein said step of converting to said cepstrum coefficients includes the steps of: segmenting said digitized speech signal at every prescribed time interval and obtaining an auto-correlation coefficient vector; and calculating a linear predictive coefficient vector based on said auto-correlation coefficient vector.

Claim 6

Dependent Claim 53 words

The speech recognition method according to claim 2, wherein said step of converting to said cepstrum coefficients includes the step of segmenting said digitized speech signal at every prescribed time interval and obtaining a logarithmic spectrum by Fourier transform and calculating a cepstrum coefficient vector by inverse Fourier transform of the logarithmic spectrum.

Claim 7

Dependent Claim 69 words

The speech recognition method according to claim 2, wherein said step of recognizing the speech includes the steps of: assigning the closest one of the centroid vectors obtained from a number of training samples of dynamic cepstrum vectors to the time sequence of centroid vectors of said dynamic cepstrum for an input speech, to generate a sequence of vector code numbers; and recognizing said sequence of vector code numbers.

Claim 8

Dependent Claim 75 words

The speech recognition method according to claim 5, further comprising the step of: collecting training samples represented by said sequence of vector code numbers and learning the same in accordance with a prescribed algorithm; wherein said step of generating said sequence of vector code numbers includes the step of recognizing a sequence of vector code numbers of the input speech to be recognized, based on the result of learning in accordance with said prescribed algorithm.

Claim 9

Dependent Claim 23 words

The speech recognition method according to claim 6, wherein said step of learning includes the step of learning by using Hidden Markov Models.

Claim 10

Dependent Claim 36 words

The speech recognition method according to claim 2, wherein said step of recognizing an input speech sound includes the step of learning the probability of the spectral features of training speed units including phenomes or words.

Claim 11

Dependent Claim 37 words

The speech recognition method according to claim 8, wherein said step of recognizing the speech includes the step of recognizing the input speech represented by the dynamic cepstrum time sequence by using the result of said learning.

Claim 12

Dependent Claim 97 words

A speech recognition method, comprising the steps of: converting an input speech to a digitized speech signal; segmenting said digitized speech signal at every prescribed time interval in order to obtain a logarithmic spectrum time sequence by Fourier transform; effecting a time frequency masking by an operation of obtaining masked speech spectrum by subtracting, from speech spectrum at present, a masking pattern which is a function of frequency obtained by smoothing immediately preceding speech spectrum by time and frequency for obtaining a masked spectrum time sequence; and recognizing the speech by using said masked spectrum time sequence.

Claim 13

Dependent Claim 97 words

A speech recognition method, comprising the steps of: converting an input speech to a digitized speech signal; segmenting said digitized speech signal at every prescribed time interval in order to obtain a logarithmic spectrum time sequence by Fourier transform; effecting a time frequency masking by an operation of obtaining masked speech spectrum by subtracting, from speech spectrum at present, a masking pattern which is a function of frequency obtained by smoothing immediately preceding speech spectrum by time and frequency for obtaining a masked spectrum time sequence; and recognizing the speech by using said masked spectrum time sequence.

Claim 14

Dependent Claim 39 words

The speech recognition method according to claim 10, wherein said step of recognizing the speech includes the step of recognizing the speech by calculating a feature vector representing the same content as a dynamic cepstrum including said masked spectrum.

Claim 15

Dependent Claim 30 words

The speech recognition method according to claim 11, wherein said step of recognizing the input speech includes the step of recognizing the speech by a method of dynamic time warping.

Claim 16

Dependent Claim 92 words

The speech recognition method according to claim 11, wherein said step of recognizing the input speech includes the steps of: storing as a template, typical speech sound of a word to be recognized as it is, or storing as a template, an average of a plurality of typical speech sounds of the word to be recognized; and calculating a distance between said registered word template and the time sequence of said masked spectrum of the input speech to be recognized by dynamic time warping, and recognizing the speech based on this distance.

Annuity Stage	Window Opens	Payment Deadline	Surcharge Period Until	Estimated Fee	Status
3.5 Years (Year 4)	1998-10-16	1999-04-17	1999-10-17	$2000.00	Window Closed / Lapsed
7.5 Years (Year 8)	2002-10-16	2003-04-17	2003-10-17	$3760.00	Window Closed / Lapsed
11.5 Years (Year 12)	2006-10-16	2007-04-17	2007-10-17	$7700.00	Window Closed / Lapsed

Speech recognition method using time-frequency masking mechanism

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