US5732392A
Expired / Lapsed
US
⏱️ 0.0 years remaining
Method for speech detection in a high-noise environment
Assignee: NTT Inc
•
Filing: 1996-09-24
•
Granted: 1998-03-24
•
Expires: 2016-09-24
Abstract
Abstract In method for detecting a speech period in a high-noise environment, the variation in the spectrum of an input signal per unit time is calculated over an analysis frame period, and when the frequency of spectrum variation falls in a predetermined range, the input signal of that frame is decided to be a speech signal.
Inventors
- 👤 Osamu Mizuno
- 👤 Satoshi Takahashi
- 👤 Shigeki Sagayama
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-10-30 03:54
CPC Classifications
G; G10; G10L; G10L25/00; G10L25/78; G10L25/03; G10L25/06; G10L25/18; G10L25/24
Claims Analysis
Total Claims: 18
Claim 1
Independent Claim
113 words
A signal processing method for detecting a speech period in an input signal, comprising the steps of: (a) obtaining a spectral feature parameter by analyzing the spectrum of said input signal for each predetermined analysis window; (b) calculating the amount of change in said spectral feature parameter of said input signal per unit time; (c) calculating the frequency of variation in the amount of said spectral feature parameter over a predetermined analysis frame period longer than said unit time; and (d) making a check to see if said frequency of variation falls in a predetermined frequency range and, if so, deciding that said input signal of said analysis frame is a speech signal.
Claim 2
Dependent Claim
113 words
A signal processing method for detecting a speech period in an input signal, comprising the steps of: (a) obtaining a spectral feature parameter by analyzing the spectrum of said input signal for each predetermined analysis window; (b) calculating the amount of change in said spectral feature parameter of said input signal per unit time; (c) calculating the frequency of variation in the amount of said spectral feature parameter over a predetermined analysis frame period longer than said unit time; and (d) making a check to see if said frequency of variation falls in a predetermined frequency range and, if so, deciding that said input signal of said analysis frame is a speech signal.
Claim 3
Dependent Claim
81 words
The method of claim 1, wherein said step of calculating the amount of change in said spectral feature parameter comprises a step of obtaining a time sequence of feature vectors representing the spectra of said input signal at respective points in time, and a step of calculating dynamic features through the use of said feature vectors at a plurality of points in time and calculating the variation in the spectrum of said input signal from the norm of said dynamic features.
Claim 4
Dependent Claim
24 words
The method of claim 2, wherein said dynamic feature are polynomial expansion coefficients of said feature vectors at a plurality of points in time.
Claim 5
Dependent Claim
43 words
The method of claim 1, 2, or 3, wherein said frequency calculating step is a step of counting the number of peaks of said spectrum variation exceeding a predetermined threshold value over said analysis frame and providing the count value as said frequency.
Claim 6
Dependent Claim
70 words
The method of claim 1, 2, or 3 wherein said frequency calculating step includes a step of calculating the sum total of variations in the spectrum of said input signal over said predetermined analysis frame period longer than said unit time and said deciding step decides that said input signal of said analysis frame period is a speech signal when said sum total falls in a predetermined range of values.
Claim 7
Dependent Claim
60 words
The method of claim 4, wherein said step of calculating said spectrum variation comprises a step of calculating a gradient vector using as its elements linear differential coefficients of respective elements of a vector representing said spectral feature parameter, and a step of calculating square-sums of said respective elements of said gradient vector as dynamic measures of said spectrum variation.
Claim 8
Dependent Claim
22 words
The method of claim 6, wherein said spectral feature parameter is an LPC cepstrum and said spectrum variation is a delta cepstrum.
Claim 9
Dependent Claim
80 words
The method of claim 1, further comprising a step of vector quantizing said input signal for each said analysis window by referring to a vector code book composed of representative vectors of spectral feature parameters of speech prepared from speech data and calculating quantization distortion; and wherein said deciding step decides that said input signal is a speech signal when said quantization distortion is smaller than a predetermined value and said frequency of variation is within said predetermined frequency range.
Claim 10
Dependent Claim
54 words
The method of claim 1, further comprising a step of detecting whether said input signal in said each analysis window is a vowel, and wherein said deciding step (d) said input signal is a speech signal when said detecting step detects a vowel and said frequency of variation is in said predetermined frequency range.
Claim 11
Dependent Claim
43 words
The method of claim 9, wherein said vowel detecting step detects a pitch frequency in said input signal for said each analysis window and decides that said input signal is a vowel when said detected pitch frequency is in a predetermined frequency range.
Claim 12
Dependent Claim
41 words
The method of claim 9, wherein said vowel detecting step detects the power of said input signal for said each analysis window and decides that said input signal is a vowel when said detected power is larger than a predetermined value.
Claim 13
Dependent Claim
38 words
The method of claim 9, wherein said vowel detecting step detects the autocorrelation value of said input signal and decides that said input signal is a vowel when said detected autocorrelation value is larger than a predetermined value.
Claim 14
Dependent Claim
88 words
The method of claim 1, further comprising a step (e) of counting the number of zero crossings of said input signal in said each analysis window and decides that said input signal in said analysis window is a consonant when said count value is within a predetermined range, and wherein said deciding step (d) decides that said input signal is a speech signal when said input signal is decided as a consonant by said deciding step (e) and said frequency of variation is in said predetermined frequency range.
Claim 15
Dependent Claim
17 words
The method of claim 1, 2, or 3, wherein said spectral feature parameter is an LPC cepstrum.
Claim 16
Dependent Claim
17 words
The method of claim 1, 2, or 3, wherein said spectral feature parameter is an FFT cepstrum.
Claim 17
Dependent Claim
60 words
The method of claim 5, wherein said step of calculating said spectrum variation comprises a step of calculating a gradient vector using as its elements linear differential coefficients of respective elements of a vector representing said spectral feature parameter, and a step of calculating square-sums of said respective elements of said gradient vector as dynamic measures of said spectrum variation.
Claim 18
Dependent Claim
22 words
The method of claim 16, wherein said spectral feature parameter is an LPC cepstrum and said spectrum variation is a delta cepstrum.
References Cited (Backward Citations)
US3712959A
Cited
US4282403A
Cited
US5220629A
Cited
US5210820A
Cited
US5459815A
Cited
US5596680A
Cited
US5598504A
Cited
US5579435A
Cited
USPTO Annuity & Maintenance Windows
| Annuity Stage | Window Opens | Payment Deadline | Surcharge Period Until | Estimated Fee | Status |
|---|---|---|---|---|---|
| 3.5 Years (Year 4) | 2001-03-23 | 2001-09-22 | 2002-03-24 | $2000.00 | Window Closed / Lapsed |
| 7.5 Years (Year 8) | 2005-03-23 | 2005-09-22 | 2006-03-24 | $3760.00 | Window Closed / Lapsed |
| 11.5 Years (Year 12) | 2009-03-23 | 2009-09-22 | 2010-03-24 | $7700.00 | Window Closed / Lapsed |