Matra Nortel Communications

Philip Lockwood

St&eacute;phane Lubiarz

Catherine Glorion

Laurent Lelievre

François Capman

Stéphane Lubiarz

Edouard Hinard

The invention concerns a method for detecting voice activity in a digital speech signal, in at least a frequency band, for example by means of a detecting automaton whereof the status is controlled on the basis of an energy analysis of the signal. The control of said automaton, or more generally the determination of voice activity, comprises a comparison, in the frequency band, of two different versions of the speech signal one of which at least is a noise-corrected version.

Method and device for detecting voice activity

The invention concerns a method for conditioning a digital speech signal(s) processed by successive frames, which consists carrying out a harmonic analysis to estimate the pitch on each frame where it has a speech activity, and in oversampling at an oversampling frequency (f ) which is a multiple of the estimated pitch.

Method for conditioning a digital speech signal

A digital speech signal processed by successive frames is subjected to noise suppression taking account of estimates of the noise included in the signal, updated for each frame in a manner dependent on at least one degree of vocal activity. 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 the energy variations of the a priori noise-suppressed signal are analyzed to detect the degree of vocal activity of said frame.

Method for detecting speech activity

A spectral subtraction is effected including: a first subtraction step in which overestimates of the spectral component of the noise are taken into account, to obtain spectral components of a first noise-suppressed signal; the computation of a masking curve by applying an auditory perception model on the basis of the spectral components of the first noise-suppressed signal; and a second subtraction step in which a respective quantity depending on parameters including a difference between the overestimate of the corresponding spectral component of the noise and the computed masking curve is subtracted from each spectral component of the speech signal in the frame. The result of the spectral subtraction is transformed into the time domain to construct a noise-suppressed speech signal.

Method for suppressing noise in a digital speech signal

A recognition test is applied to a segment of speech produced by a speaker. An examination is carried out to see whether a first optimum model, with which the recognition test has matched the spoken segment of speech, satisfies a criterion of likelihood. If the criterion of likelihood is satisfied, this first optimum model is selected. If not, a repetition of the speech segment is requested. The recognition test is applied to the segment of speech repeated by the speaker. This second optimum model is selected if it satisfies the likelihood criterion. If not, and if a combination of the results of the two recognition tests satisfies at least one criterion of combined selection, one of the models which the two recognition tests have matched with the spoken segment of speech and with the repeated segment is selected.

Method of speech recognition

A recognition test matches a speech segment supplied to the system with a set of parameters associated with a reference and memorized in a dictionary. A provisional version of each set of parameters to be memorized in the dictionary in association with a reference is estimated on the basis of one or more segments of speech, after which the provisional version is memorized in the dictionary in association with the reference. At least one repetition of the speech segment is submitted to a recognition test, after which depending on whether it has matched the speech segment with the provisional version, the provisional version is modified and the modified provisional version is memorized.

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Vaureal, France

Philip Lockwood