Source: http://www.google.com/patents/US5732392?dq=6,205,432
Timestamp: 2016-06-25 08:44:01
Document Index: 238066737

Matched Legal Cases: ['art 15', 'art 16', 'art 15', 'art 21', 'art 21', 'art 15', 'art 15', 'art 21', 'art 21']

Patent US5732392 - Method for speech detection in a high-noise environment - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign inPatentsIn 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...http://www.google.com/patents/US5732392?utm_source=gb-gplus-sharePatent US5732392 - Method for speech detection in a high-noise environmentAdvanced Patent SearchPublication numberUS5732392 APublication typeGrantApplication numberUS 08/719,015Publication dateMar 24, 1998Filing dateSep 24, 1996Priority dateSep 25, 1995Fee statusLapsedAlso published asDE69613646D1, DE69613646T2, EP0764937A2, EP0764937A3, EP0764937B1Publication number08719015, 719015, US 5732392 A, US 5732392A, US-A-5732392, US5732392 A, US5732392AInventorsOsamu Mizuno, Satoshi Takahashi, Shigeki SagayamaOriginal AssigneeNippon Telegraph And Telephone CorporationExport CitationBiBTeX, EndNote, RefManPatent Citations (8), Referenced by (56), Classifications (11), Legal Events (6) External Links: USPTO, USPTO Assignment, EspacenetMethod for speech detection in a high-noise environment
US 5732392 AAbstract
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.
1. 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. 2. 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.
The present invention relates to a speech endpoint detecting method and, more particularly, to a method for detecting a speech period from a speech-bearing signal in a high-noise environment.
It is therefore an object of the present invention to provide a signal processing method which permits stable detection of the speech period from the input signal even in a high-noise environment through utilization of information characteristic of speech.
FIG. 1 is a graph showing the frequency of spectrum change of a speech signal on which the present invention os based;
In accordance with the present invention, a spectrum variation of the input signal is derived from a time sequence of its spectral feature parameters and the speech period to be detected is a period over which the spectrum of the input signal changes with about the same frequency as in the speech period.
The spectral parameter by the LPC cepstrum analysis is expressed in the same form as Eq. (3). Furthermore, a linear prediction coefficient {α1 |i=1, . . . , p}, a PARCOR coefficient {Ki |i=1, . . . , p} and a line spectrum pair LSP also represent spectral envelope information of speech signals. These spectral parameters are all expressed by a coefficient sequence (vector) and are called acoustic feature vectors. A description will be given typically of the LPC cepstrum C={c1, c2, . . . , cK }, but any other spectral parameters can also be used.
As referred to previously herein, the principle of the present invention is to decide whether the period of the input signal is a speech period, depending on whether the frequency of spectrum variation of the input signal is within a predetermined range. The amount of change in the spectrum is obtained as a dynamic measure of speech as described below. The first step is to obtain a time sequence of acoustic parameter vectors of the speech signal by the FFT analysis, LPC analysis or some other spectrum analysis. Let it be assumed that a k-dimensional LPC cepstrum C(t)={c1, c2, . . . , cK } is used as the feature vector at time t. Next, to represent a change in the frequency spectrum of speech over a window width 2n (n being the number of discrete points in time) of a certain period, a local movement of the cepstrum C(t) is linearly approximated by a weighted least squares method and its inclination A(t) (a linear differential coefficient) is obtained as the amount of change in the spectrum (a gradient vector). That is, setting the weight wi =w-i, the inclination by the linear approximation is given by the following equation: ##EQU4## In the above, ak (t) represents a k-th element of a k-dimensional vector A(t)={a1 (t), a2 (t), . . . , ak (t)} which represents the dynamic feature of the spectrum at time t, and A(t) is referred to as a delta cepstrum. That is, ak (t) indicates a linear differential coefficient of a time sequence of k-dimensional cepstrum elements ck (t) at time t (see Furui, "Digital Speech Signal Processing," Tokai University Press).
The dynamic measure D(t) at time t is calculated by the following equation which represents the sum of squares of all elements of the delta cepstrum at time t (see Shigeki Sagayama and Fumitada Itakura, "On Individuality in a Dynamic Measure of Speech," Proc. Acoustical Society of Japan Spring Conf. 1979, 3-2-7, pp.589-590, June 1979). ##EQU5## That is, the cepstrum C(k) represents the feature of the spectral envelope and the delta cepstrum, which is its linear differential coefficient, represents the dynamic feature. Hence, the dynamic measure represents the magnitude of the spectrum variation. The frequency SF of the spectrum variation is calculated as the number of peaks of the dynamic measures D(t) that exceed a predetermined threshold value Dth during a certain frame period F (an analysis frame), or as the sum total (integral) of the dynamic measures D(t) in the analysis frame F.
By performing the above-described processing over the analysis frame F of a 400 msec time length considered to contain a plurality of phonemes, 40 dynamic measures D(t) are obtained. A speech period detecting part 15 counts the number of peaks of those of the dynamic measures D(t) which exceed the threshold value Dth and provides the count value as the frequency SF of the spectrum variation. Alternatively, the sum total of the dynamic measures D(t) over the analysis frame F is calculated and is defined as the frequency SF of the spectrum variation.
The frequency of spectrum variation in the speech period is precalculated, on the basis of which the upper and lower limit threshold values are predetermined. The frame of the input signal which falls in the range from the upper and lower limit threshold values is detected as a speech frame. Finally, the speech period detected result is output from a detected speech period output part 16. By repeatedly obtaining the frequency SF of spectrum variation during the application of the input signal while shifting the temporal position of the analysis frame F by a time interval of 20 msec each time, the speech period in the input signal is detected.
FIG. 5 is a diagram for explaining an example of the result of detection of speech with noise superimposed thereon. The input signal waveform shown on Row A was prepared as follows: The noise of a moving car was superimposed, with a 0 dB SN ratio, on a signal obtained by concatenating two speakers' utterances of a Japanese word /aikawarazu/ which means "as usual," the utterances being separated by a 5 sec silent period. Row B in FIG. 5 shows a correct speech period representing the period over which speech is present, Row D shows variations in the dynamic measure D(t). Row C shows the speech period detected result automatically determined on the basis of variations in the dynamic measure D(t). The dynamic measure D(t) was obtained under the same conditions as in FIG. 4. Accordingly, the dynamic measure was obtained every 10 ms. The analysis frame length was 400 ms and the analysis frame was shifted in steps of 200 ms. The sum total of the dynamic measures D(t) in the analysis frame period was calculated as the frequency SF of the spectrum variation. In this example, the analysis frame F for which the value of this sum total exceeded a predetermined value 4.0 was detected as the speech period. While speech periods are not clearly seen on the input signal waveform because of low SN ratio, it can be seen that all speech periods were detected by the method of the present invention. FIG. 5 indicates that the present invention utilizes the frequency of the spectrum variation and hence permits detection of speech in noise.
The speech period detecting part 15 decides that a signal over the 400 ms analysis frame period is a speech signal when the frequency SF of change in the dynamic measure falls in the range defined by the upper and lower limit threshold values and the quantization distortion between the feature vector of the input signal and the corresponding representative speech feature vector is smaller than a predetermined value. Although this embodiment uses the vector quantization distortion to examine the feature of the spectral enveloped it is also possible to use a time sequence of vector quantized codes to determine if it is a sequence characteristic of speech. Further, a method of obtaining a speech decision space in a spectral feature space may sometimes be employed,
FIG. 8 illustrates another embodiment of the present invention which combines the FIG. 2 embodiment with the vowel detection scheme. No description will be given of steps 12 to 16 in FIG. 8 since they corresponds to those in FIG. 2. A vowel detecting part 21 detects the pitch frequency, for instance. The vowel detecting part 21 detects the pitch frequency in the input signal and provides it to the speech period detecting part 15. The speech period detecting part 15 determines if the frequency SF of the variation in the dynamic measure D(t) is in the predetermined threshold value range in the same manner as in the above and decides whether the pitch frequency is in the 50 to 500 Hz range typically of human speech. An input signal frame which satisfies these two conditions is detected as a speech frame. In FIG. 8 the vowel detecting part 21 is shown to be provided separately of the main processing steps 12 through 16, but since in practice the pitch frequency, spectral power or autocorrelation value can be obtained by calculation in step 13 in the course of cepstrum calculation, the vowel detecting part 21 need not always be provided separately. While in FIG. 8 the detection of the pitch frequency is shown to be used for the detection of the speech vowel period, it is also possible to calculate one or more of the pitch frequency, power and autocorrelation value and use them for the decision of the speech signal.
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22, 1996ASAssignmentOwner name: NIPPON TELEGRAPH AND TELEPHONE CORPORATION, JAPANFree format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:MIZUNO, OSAMU;TAKAHASHI, SATOSHI;SAGAYAMA, SHIGEKI;REEL/FRAME:008256/0065Effective date: 19960925Sep 21, 2001FPAYFee paymentYear of fee payment: 4Jul 1, 2005FPAYFee paymentYear of fee payment: 8Oct 26, 2009REMIMaintenance fee reminder mailedMar 24, 2010LAPSLapse for failure to pay maintenance feesMay 11, 2010FPExpired due to failure to pay maintenance feeEffective date: 20100324RotateOriginal ImageGoogle Home - Sitemap - USPTO Bulk Downloads - Privacy Policy - Terms of Service - About Google Patents - Send FeedbackData provided by IFI CLAIMS Patent Services