EP1016071B1 - Method and apparatus for detecting speech activity - Google Patents

Description

The present invention relates to techniques digital speech signal processing. She relates more particularly to techniques making call for voice activity detection to perform differentiated processing depending on whether the signal supports or not a voice activity.

The digital techniques in question come under various fields: speech coding for transmission or storage, speech recognition, decreased noise, echo cancellation ...

Voice activity detection methods have main difficulty distinguishing between activity voice and the accompanying noise. The use of a conventional denoising technique does not allow treatment this difficulty, since these techniques do themselves use noise estimates that depend on degree signal voice activity. This problem has for example been described in document US-A-5659622.

A main object of the present invention is to improve the noise robustness of the voice activity detection. To achieve this goal, a method as claimed in claim 1 is provided.

The invention thus provides a detection method voice activity in a digital speech signal processed by successive frames, in which the speech signal to denoising taking into account noise estimates included in the signal, updated day for each frame in a dependent manner of at least a degree of vocal activity determined for said frame. According to the invention, a priori denoising is carried out of the speech signal of each frame based on estimates noise obtained when processing at least one frame previous, and we analyze the energy variations of the a priori denoised signal to detect the level of activity voice of said frame.

Proceeding with activity detection vocal (according to a method which can generally be any known method) on the basis of an a priori denoised signal significantly improves the performance of this detection when the surrounding noise is relatively high.

In the remainder of this description, we illustrate the method of detecting voice activity according to the invention in a denoising system of a signal word. It will be understood that this process can find applications in many other types of processing digital speech in which we want to have information on the degree of vocal activity of the signal processed: coding, recognition, echo cancellation ...

• la figure 1 est un schéma synoptique d'un système de débruitage mettant en oeuvre la présente invention ;
• les figures 2 et 3 sont des organigrammes de procédures utilisées par un détecteur d'activité vocale du système de la figure 1 ;
• la figure 4 est un diagramme représentant les états d'un automate de détection d'activité vocale ;
• la figure 5 est un graphique illustrant les variations d'un degré d'activité vocale ;
• la figure 6 est un schéma synoptique d'un module de surestimation du bruit du système de la figure 1 ;
• la figure 7 est un graphique illustrant le calcul d'une courbe de masquage ; et
• la figure 8 est un graphique illustrant l'exploitation des courbes de masquage dans le système de la figure 1.

Other features and advantages of the present invention will appear in the description below of nonlimiting exemplary embodiments, with reference to the appended drawings, in which:

• Figure 1 is a block diagram of a denoising system implementing the present invention;
• Figures 2 and 3 are flowcharts of procedures used by a voice activity detector of the system of Figure 1;
• FIG. 4 is a diagram representing the states of a voice activity detection automaton;
• FIG. 5 is a graph illustrating the variations of a degree of vocal activity;
• Figure 6 is a block diagram of a noise overestimation module of the system of Figure 1;
• FIG. 7 is a graph illustrating the calculation of a masking curve; and
• FIG. 8 is a graph illustrating the use of the masking curves in the system of FIG. 1.

The denoising system shown in FIG. 1 processes a digital speech signal s. A windowing module 10 puts this signal s in the form of successive windows or frames, each consisting of a number N of digital signal samples. Conventionally, these frames can have mutual overlaps. In the following of this description, it will be considered, without this being limiting, that the frames consist of N = 256 samples at a sampling frequency F _e of 8 kHz, with a Hamming weighting in each window, and 50% overlap between consecutive windows.

The signal frame is transformed in the frequency domain by a module 11 applying a conventional algorithm this fast Fourier transform (TFR) to calculate the module of the signal spectrum. The module 11 then delivers a set of N = 256 frequency components of the speech signal, denoted S _{n, f} , where n denotes the number of the current frame, and f a frequency of the discrete spectrum. Due to the properties of digital signals in the frequency domain, only the first N / 2 = 128 samples are used.

To calculate the noise estimates contained in the signal s, the frequency resolution available at the output of the fast Fourier transform is not used, but a lower resolution, determined by a number I of frequency bands covering the band [0 , F _e / 2] of the signal. Each band i (1≤i≤I) extends between a lower frequency f (i-1) and a higher frequency f (i), with f (0) = 0, and f (I) = F _e / 2 . This division into frequency bands can be uniform (f (i) -f (i-1) = F _e / 2I). It can also be non-uniform (for example according to a barks scale). A module 12 calculates the respective averages of the spectral components S _{n, f} of the speech signal in bands, for example by a uniform weighting such that:

This averaging reduces the fluctuations between the bands by averaging the noise contributions in these bands, which will decrease the variance of the estimator of noise. In addition, this averaging allows a large reduction of the complexity of the system.

The averaged spectral components S _{n, i} are addressed to a voice activity detection module 15 and to a noise estimation module 16. These two modules 15, 16 operate jointly, in the sense that degrees of vocal activity γ _{n, i} measured for the different bands by the module 15 are used by the module 16 to estimate the long-term energy of the noise in the different bands, while these long-term estimates Ê _{n, i} are used by the module 15 to carry out a priori denoising of the speech signal in the different bands to determine the degrees of vocal activity γ _{n, i} .

The operation of modules 15 and 16 can correspond to the flowcharts represented in the figures 2 and 3.

In steps 17 to 20, the module 15 proceeds a priori to denoising the speech signal in the different bands i for the signal frame n. This a priori denoising is carried out according to a conventional process of non-linear spectral subtraction from noise estimates obtained during one or more previous frames. In step 17, the module 15 calculates, with the resolution of the bands i, the frequency response Hp _{n, i} of the a priori denoising filter, according to the formula: hp or = S or - α ' not -τ1 i . B not -τ1 i S not -τ2 i where τ1 and τ2 are delays expressed in number of frames (τ1≥1, τ2≥0), and α _{'n, i} is a noise overestimation coefficient whose determination will be explained later. The delay τ1 can be fixed (for example τ1 = 1) or variable. The lower the confidence in the detection of voice activity.

où βp_i est un coefficient de plancher proche de 0, servant classiquement à éviter que le spectre du signal débruité prenne des valeurs négatives ou trop faibles qui provoqueraient un bruit musical.In steps 18 to 20, the spectral components pp _{n, i} are calculated according to:

where βp _i is a floor coefficient close to 0, conventionally used to prevent the spectrum of the denoised signal from taking negative or too low values which would cause musical noise.

Steps 17 to 20 therefore essentially consist in subtracting from the signal spectrum an estimate, increased by the coefficient α ' _{n-τ1, i} , of the noise spectrum estimated a priori.

In step 21, the module 15 calculates the energy of the a priori denoised signal in the different bands i for the frame n: E _{n, i} = Êp 2 / n, i. It also calculates an overall average E _{n, 0} of the energy of the a priori denoised signal, by a sum of the energies per band E _{n, i} , weighted by the widths of these bands. In the notations below, the index i = 0 will be used to designate the overall band of the signal.

In steps 22 and 23, the module 15 calculates, for each band i (0≤i≤I), a quantity ΔE _{n, i} representing the short-term variation of the energy of the noise-suppressed signal in the band i, as well as long-term value E _{n, i} of the energy of the denoised signal in band i. The quantity ΔE _{n, i} can be calculated by a simplified derivation formula: .DELTA.E or = E not -4 i + E not -3 i - E not -1 i - E or 10 . As for the long-term energy E _{n, i} , it can be calculated using a forgetting factor B1 such that 0 <B1 <1, namely E _{n, i} = B 1. E _{n -1, i} + (1- B 1). E _{n, i} .

After calculating the energies E _{n, i} of the denoised signal, its short-term variations ΔE _{n, 1} and its long-term values E _{n, i} in the manner indicated in FIG. 2, the module 15 calculates, for each band i (0 i i I I), a value ρ ₁ representative of the evolution of the energy of the denoised signal. This calculation is carried out in steps 25 to 36 of FIG. 3, executed for each band i between i = 0 and i = 1. This calculation uses a long-term estimator of the noise envelope ba _i , an internal estimator bi _i and a noisy frame counter b _i .

In step 25, the quantity ΔE _{n, i} is compared with a threshold ε1. If the threshold ε1 is not reached, the counter b _i is incremented by one unit in step 26. In step 27, the long-term estimator ba _i is compared to the value of the smoothed energy E _{n, i} . If ba _i ≥ E _{n, i} , the estimator ba _i is taken equal to the smoothed value E _{n, i} in step 28, and the counter b _i is reset to zero. The quantity ρ _i , which is taken equal to the ratio ba _i / E _{n, i} (step 36), is then equal to 1.

If step 27 shows that ba _i < E _{n, i} , the counter b _i is compared with a limit value bmax in step 29. If b _i > bmax, the signal is considered to be too stationary to support vocal activity. The aforementioned step 28, which amounts to considering that the frame contains only noise, is then executed. If b _i ≤bmax in step 29, the internal estimator bi _i is calculated in step 33 according to: bi i = (1- bm ). E or + bm . ba i In this formula, Bm represents an update coefficient between 0.90 and 1. Its value differs depending on the state of a voice activity detection automaton (steps 30 to 32). This state δ _n-1 is that determined during the processing of the previous frame. If the automaton is in a speech detection state (δ _n-1 = 2 in step 30), the coefficient Bm takes a value Bmp very close to 1 so that the noise estimator is very slightly updated in the presence of speech. Otherwise, the coefficient Bm takes a lower value Bms, to allow a more significant update of the noise estimator in the phase of silence. In step 34, the difference ba _i -bi _i between the long-term estimator and the internal noise estimator is compared to a threshold ε2. If the threshold ε2 is not reached, the long-term estimator ba _i is updated with the value of the internal estimator bi _i in step 35. Otherwise, the long-term estimator ba _i remains unchanged . This avoids that sudden variations due to a speech signal lead to an update of the noise estimator.

After having obtained the quantities ρ _i , the module 15 proceeds to the voice activity decisions in step 37. The module 15 first updates the state of the detection automaton according to the quantity ρ ₀ calculated for l of the signal band. The new state δ _n of the automaton depends on the previous state δ _n-1 and on ρ ₀ , as shown in Figure 4.

Four states are possible: δ = 0 detects silence, or absence of speech; δ = 2 detects the presence of voice activity; and the states δ = 1 and δ = 3 are intermediate states of ascent and descent. When the automaton is in the state of silence (δ _n-1 = 0), it remains there if ρ ₀ does not exceed a first threshold SE1, and it goes into the state of ascent otherwise. In the rising state (δ _n-1 = 1), it returns to the state of silence if ρ ₀ is smaller than the threshold SE1, it goes into the speaking state if ρ ₀ is greater than a second threshold SE2 greater than the threshold SE1, and it remains in the rising state if SE1≤ ρ ₀ ≤SE2. When the automaton is in the speech state (δ _n-1 = 2), it remains there if ρ ₀ exceeds a third threshold SE3 smaller than the threshold SE2, and it goes into the descent state in the case opposite. In the descending state (δ _n-1 = 3), the automaton returns to the speaking state if ρ ₀ is greater than the threshold SE2, it returns to the silent state if ρ ₀ is below a fourth threshold SE4 smaller than the threshold SE2, and it remains in the descent state if SE4≤ρ ₀ ≤SE2.

In step 37, the module 15 also calculates the degrees of vocal activity γ _{n, i} in each band i≥1. This degree γ _{n, i} is preferably a non-binary parameter, that is to say that the function γ _{n, i} = g (ρ _i ) is a function continuously varying between 0 and 1 depending on the values taken by the magnitude ρ _i . This function has for example the appearance shown in FIG. 5.

Module 16 calculates the band noise estimates, which will be used in the denoising process, using the successive values of the components S _{n, i} and the degrees of voice activity γ _{n, i} . This corresponds to steps 40 to 42 of FIG. 3. In step 40, it is determined whether the voice activity detection machine has just gone from the rising state to the speaking state. If so, the last two estimates B and _{n -1, i} and B and _{n -2, i} previously calculated for each band i≥1 are corrected according to the value of the previous estimate B and _{n -3, i} . This correction is made to account for the fact that, in the ascent phase (δ = 1), the long-term noise energy estimates in the voice activity detection process (steps 30 to 33) may have been be calculated as s1 the signal contained only noise (Bm = Bms), so that they risk being tainted with error.

où λ_B désigne un facteur d'oubli tel que 0<λ_B<1. La formule (6) met en évidence la prise en compte du degré d'activité vocale non binaire γ_n,1.In step 42, the module 16 updates the noise estimates per band according to the formulas:

where λ _B denotes a forgetting factor such as 0 <λ _B <1. Formula (6) shows that the degree of non-binary vocal activity γ _{n, 1 is} taken into account.

As indicated above, the long-term noise estimates B and _{n, i} are overestimated, by a module 45 (FIG. 1), before proceeding to denoising by nonlinear spectral subtraction. Module 45 calculates the overestimation coefficient α ' _{n, i} previously mentioned, as well as an increased estimate B and ' _{n, i} which essentially corresponds to α ' _{n, i} . B and _{n, i} .

The organization of the overestimation module 45 is shown in FIG. 6. The enhanced estimate B and ' _{n, i} is obtained by combining the long-term estimate B and _{n, i} and a measure ΔB max / n, i of the variability of the noise component in band i around its long-term estimate. In the example considered, this combination is essentially a simple sum made by an adder 46. It could also be a weighted sum.

The overestimation coefficient α ' _{n, i} is equal to the ratio between the sum B and _{n, i} + Δ B max / n, i delivered by the adder 46 and the delayed long-term estimate B and _{n -τ3, i} (divider 47), capped at a limit value α _max , for example α _max = 4 (block 48). The delay τ3 is used to correct, if necessary, in the rise phases (δ = 1), the value of the overestimation coefficient α ' _{n, i} , before the long-term estimates have been corrected by steps 40 and 41 of Figure 3 (for example τ3 = 3)

The increased estimate B and ' _{n, i} is finally taken equal to α' _{n, i} .B and _{n-τ 3 , i} (multiplier 49).

The measure Δ B max / n, i of the noise variability reflects the variance of the noise estimator. It is obtained as a function of the values of S _{n, i} and of B and _{n, i} calculated for a certain number of previous frames on which the speech signal does not present any vocal activity in the band i. It is a function of deviations | S _{nk, i} - B and _{nk, i} | calculated for a number K of frames of silence (nk≤n). In the example shown, this function is simply the maximum (block 50). For each frame n, the degree of vocal activity γ _{n, i} is compared with a threshold (block 51) to decide whether the deviation | S _{n, i} - B and _{n, i} |, calculated in 52-53, must or not be loaded into a queue 54 of K locations organized in first-in-first-out (FIFO) mode. If γ _{n, i} does not exceed the threshold (which can be equal to 0 if the function g () has the form of FIG. 5), the FIFO 54 is not supplied, while it is in the opposite case. The maximum value contained in FIFO 54 is then provided as a measure of variability Δ B max / n, i .

.The measure of variability Δ B max / n, i can, as a variant, be obtained as a function of the values S _{n, f} (and not S _{n, i} ) and B and _{n, i} . We then proceed in the same way, except that FIFO 54 does not contain | S _{nk, i} - B and _{nk, i} | for each of the bands i, but rather

.

Thanks to independent estimates of long-term fluctuations in noise B and _{n, i} and its short-term variability Δ B max / n, i , the enhanced estimator. B and ' _{n, i} provides excellent robustness to the musical noises of the denoising process.

A first phase of the spectral subtraction is carried out by the module 55 shown in FIG. 1. This phase provides, with the resolution of the bands i (1 i i I I), the frequency response H 1 / n, i of first denoising filter, as a function of the components S _{n, i} and B and _{n, i} and the overestimation coefficients α ' _{n, i} . This calculation can be performed for each band i according to the formula: H 1 or = max { S or - α ' or . B or , β 1 i . B or } S not -τ4, i where τ4 is a determined integer delay such as τ4≥0 (for example τ4 = 0). In expression (7), the coefficient β 1 / i represents, like the coefficient β p _i of formula (3), a floor conventionally used to avoid negative or too low values of the denoised signal.

As is known (EP-A-0 534 837), the overestimation coefficient α ' _{n, i} could be replaced in formula (7) by another coefficient equal to a function of α' _{n, i} and an estimate of the signal-to-noise ratio (for example S _{n, i} / B and _{n, i} ), this function decreasing according to the estimated value of the signal-to-noise ratio. This function is then equal to α ' _{n, i} for the lowest values of the signal-to-noise ratio. Indeed, when the signal is very noisy, it is a priori not useful to reduce the overestimation factor. Advantageously, this function decreases to zero for the highest values of the signal / noise ratio. This protects the most energetic areas of the spectrum, where the speech signal is most significant, the amount subtracted from the signal then tending towards zero.

This strategy can be refined by applying it selectively to frequency harmonics pitch of the speech signal when it has voice activity.

Thus, in the embodiment shown in FIG. 1, a second denoising phase is carried out by a module 56 for protecting harmonics. This module calculates, with the resolution of the Fourier transform, the frequency response H 2 / n, f of a second denoising filter as a function of the parameters H 1 / n, i , α ' _{n, i} , B and _{n , i} , δ _n , S _{n, i} and the tone frequency f _p = F _e / T _p calculated outside the phases of silence by a harmonic analysis module 57. In the phase of silence (δ _n = 0), the module 56 is not in service, that is to say that H 2 / n, f = H 1 / n, i for each frequency f of a band i. The module 57 can apply any known method of analysis of the speech signal of the frame to determine the period T _p , expressed as an integer or fractional number of samples, for example a linear prediction method.

The protection provided by the module 56 may consist in carrying out, for each frequency f belonging to a band i:

Δf = F _e / N represents the spectral resolution of the Fourier transform. When H 2 / n, f = 1, the quantity subtracted from the component S _{n, f} will be zero. In this calculation, the floor coefficients β 2 / i (for example β 2 / i = β 1 / i ) express the fact that certain harmonics of the tone frequency f _p can be masked by noise, so that n protecting them is useless.

This protection strategy is preferably applied for each of the frequencies closest to the harmonics of f _p , that is to say for any arbitrary integer.

If we denote by δf _p the frequency resolution with which the analysis module 57 produces the estimated tone frequency f _p , that is to say that the real tone frequency is between f _p -δf _p / 2 and f _p + δf _p / 2, then the difference between the η-th harmonic of the real tonal frequency is its estimate η × f _p (condition (9)) can go up to ± η × δf _p / 2. For high values of η, this difference can be greater than the spectral half-resolution Δf / 2 of the Fourier transform. To take account of this uncertainty and to guarantee the good protection of the harmonics of the real tonal frequency, each of the frequencies can be protected. of the interval [η × f _p - η × δ f _p / 2, η × f _p + η × δ f _p / 2], i.e. replace condition (9) above by: ∃η integer / f - η. f p ≤ (η.δ f p + Δ f ) / 2 This way of proceeding (condition (9 ')) is of particular interest when the values of η can be large, in particular in the case where the method is used in a broadband system.

For each protected frequency, the corrected frequency response H 2 / n, f can be equal to 1 as indicated above, which corresponds to the subtraction of a zero quantity in the context of spectral subtraction, that is to say ie full protection of the frequency in question. More generally, this corrected frequency response H 2 / n, f could be taken equal to a value between 1 and H 1 / n, f depending on the degree of protection desired, which corresponds to the subtraction of an amount less than which would be subtracted if the frequency in question was not protected.

The spectral components S 2 / n, f of a denoised signal are calculated by a multiplier 58: S 2 n, f = H 2 n, f . S n, f

This signal S 2 / n, f is supplied to a module 60 which calculates, for each frame n, a masking curve by applying a psychoacoustic model of auditory perception by the human ear.

The masking phenomenon is a principle known from functioning of the human ear. When two frequencies are heard simultaneously, it is possible that one of the two is no longer audible. We say then that it is hidden.

où les indices q et q' désignent les bandes de bark (0≤q,q'≤Q), et S 2 / n,q, représente la moyenne des composantes S 2 / n,f du signal excitateur débruité pour les fréquences discrètes f appartenant à la bande de bark q'.There are different methods for calculating masking curves. One can for example use that developed by JD Johnston ("Transform Coding of Audio Signals Using Perceptual Noise Criteria", IEEE Journal on Selected Area in Communications, Vol. 6, No. 2, February 1988). In this method, we work in the frequency scale of the barks. The masking curve is seen as the convolution of the spectral spreading function of the basilar membrane in the bark domain with the excitatory signal, constituted in the present application by the signal S 2 / n, f . The spectral spreading function can be modeled as shown in Figure 7. For each bark band, we calculate the contribution of the upper and lower bands convoluted by the spreading function of the basilar membrane:

where the indices q and q 'denote the bark bands (0≤q, q'≤Q), and S 2 / n, q , represents the mean of the components S 2 / n, f of the excitatory signal denoised for the discrete frequencies f belonging to the bark band q '.

où SFM représente, en décibels, le rapport entre la moyenne arithmétique et la moyenne géométrique de l'énergie des bandes de bark, et SFM_max=-60 dB.The masking threshold M _{n, q} is obtained by the module 60 for each bark band q, according to the formula: M n, q = C n, q / R q where R _q depends on the more or less voiced character of the signal. As is known, a possible form of R _q is: 10.log 10 (R q ) = (A + q) .χ + B. (1-χ) with A = 14.5 and B = 5.5. χ designates a degree of voicing of the speech signal, varying between zero (no voicing) and 1 (strongly voiced signal). The parameter χ can be of the known form:

where SFM represents, in decibels, the ratio between the arithmetic mean and the geometric mean of the energy of the bark bands, and SFM _max = -60 dB.

The denoising system also includes a module 62 which corrects the frequency response of the denoising filter, as a function of the masking curve M _{n, q} calculated by the module 60 and of the increased estimates B and ' _{n, i} calculated by the module 45. The module 62 decides the level of denoising which must really be reached.

By comparing the envelope of the estimate increased by the noise with the envelope formed by the masking thresholds M _{n, q} , it is decided to denoise the signal only insofar as the estimate increased B and ' _{n, i} exceeds the masking curve. This avoids unnecessarily removing noise masked by speech.

The new response H 3 / n, f , for a frequency f belonging to the band i defined by the module 12 and to the bark band q, thus depends on the relative difference between the increased estimate B and ' _{n, i} of the corresponding spectral component of the noise and the masking curve M _{n, q} , as follows:

In other words, the quantity subtracted from a spectral component S _{n, f} , in the process of spectral subtraction having the frequency response H ³ _{n, f} , is substantially equal to the minimum between on the one hand the quantity subtracted from this spectral component in the spectral subtraction process having the frequency response H ² _{n, f} , and on the other hand the fraction of the increased estimate B and ' _{n, i} of the corresponding spectral component of the noise which, if necessary, exceeds the masking curve M _{n, q} .

FIG. 8 illustrates the principle of the correction applied by the module 62. It schematically shows an example of a masking curve M _{n, q} calculated on the basis of the spectral components S ² _{n, f} of the noise-suppressed signal, as well as the increased estimate B and ' _{n, i} of the noise spectrum. The quantity finally subtracted from the components S _{n, f} will be that represented by the hatched areas, that is to say limited to the fraction of the increased estimate B and ' _{n, i} of the spectral components of the noise which exceeds the curve of masking.

This subtraction is carried out by multiplying the frequency response H 3 / n, f of the denoising filter by the spectral components S _{n, f} of the speech signal (multiplier 64). A module 65 then reconstructs the denoised signal in the time domain, by operating the inverse fast Fourier transform (TFRI) which reverses samples of frequency S 3 / n, f delivered by the multiplier 64. For each frame, only the N / 2 = 128 first samples of the signal produced by module 65 are delivered as final denoised signal s ³ , after reconstruction by addition-overlap with the N / 2 = 128 last samples of the previous frame (module 66).

Claims (8)

1. Method of detecting vocal activity in a digital speech signal (s) processed by successive frames, comprising the step of subjecting the speech signal to noise suppression taking account of estimates of the noise included in the signal, updated for each frame in a manner depending on at least one degree of vocal activity (γ _n,i ) determined for said frame, characterized in that a priori noise suppression is applied to the speech signal of each frame on the basis of estimates of the noise obtained on processing at least one preceding frame, and energy variations of the a priori noise-suppressed signal are analyzed to detect the degree of vocal activity of said frame.
2. Method according to claim 1, wherein the degree of vocal activity (γ _n,i ) is a non-binary parameter.
3. Method according to claim 2, wherein the degree of vocal activity (γ_n,i) is a function which varies in a continuous manner in the range from 0 to 1.
4. Method according to any one of the preceding claims, wherein the estimates of the noise are obtained in different frequency bands of the signal, the a priori noise suppression is effected band by band, and a degree of vocal activity (γ_n,i) is determined for each band.
5. Method according to any one of the preceding claims, wherein an estimate of the noise B and_n,i is obtained for the frame n in a band of frequencies i in the form:

   

   where

   

   where λ_B is a forgetting factor in the range from 0 to 1, γ_n,i is the degree of vocal activity determined for the frame n in the band of frequencies i, and S_n,i is an average speech signal amplitude in frame n in band i.
6. Method according to claim 5, in which the a priori noise-suppressed signal Êp_n,i relative to a frame n and a band of frequencies i is of the form:

   

   where Hpn,i = S n,i - α' n-τ1,i . B n-τ1,i S n-τ2,i , τ1 is an integer at least equal to 1, τ2 is an integer at least equal to 0, α'_{n-τ1, i} is an overestimation coefficient determined for the frame n-τ1 and the band i, and βp_i is a positive coefficient.
7. Method according to any one of the preceding claims, wherein a long-term estimate ( E _n,i) of the energy of the a priori noise-suppressed signal (Êp_n,i ) is computed and said long-term estimate is compared with an instantaneous estimate (ba) of said energy, computed over the current frame, to obtain the degree of vocal activity (γ_n,i) of said frame.
8. A vocal activity detector, comprising processing means adapted to implement a method according to any one of the preceding claims.

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