Method and device for voice recognition in environments with fluctuating noise levels
First Claim
1. A method of voice recognition in a noise-ridden acoustic signal comprising:
- a step of digitizing and subdividing the noise-ridden acoustic signal into a sequence of temporal frames;
a step of parameterizing speech-containing temporal frames so as to obtain a vector of parameters, per speech containing frame, in the frequency domain, the vector of parameters expressing the acoustic contents of each speech containing frame;
a shape-recognition step in which the vectors of parameters are assessed with respect to references pre-recorded in a reference space during a preliminary learning step, so as to obtain recognition by the determining of at least one reference which is closest to the vector of parameters;
a step of reiterative searching for successive noise models in the sequence of temporal frames, a new noise model replacing a current noise model, each noise model comprising several successive frames;
a step of searching for a noise transition between the new noise model and the current noise model; and
wherein, when the noise transition has been detected, the method comprises a step of updating the reference space as a function of the new noise model, the parameterizing step including a step of matching the parameters to the new noise model.
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Abstract
A method of voice recognition in a noise-ridden acoustic signal comprises a phase of digitizing temporal frames of the noise-ridden acoustic signal, a phase of parametrization of speech-containing temporal frames, a shape-recognition phase in which the parameters are assessed with respect to references pre-recorded in a reference space, a phase of reiterative searching for noise models in the noise-ridden signal frames, a phase of searching for a transition between the new noise model and the old model and, when the noise transition has been detected, a phase of updating the reference space, the parametrization phase including a step of matching the parameters to the new noise model.
50 Citations
19 Claims
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1. A method of voice recognition in a noise-ridden acoustic signal comprising:
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a step of digitizing and subdividing the noise-ridden acoustic signal into a sequence of temporal frames;
a step of parameterizing speech-containing temporal frames so as to obtain a vector of parameters, per speech containing frame, in the frequency domain, the vector of parameters expressing the acoustic contents of each speech containing frame;
a shape-recognition step in which the vectors of parameters are assessed with respect to references pre-recorded in a reference space during a preliminary learning step, so as to obtain recognition by the determining of at least one reference which is closest to the vector of parameters;
a step of reiterative searching for successive noise models in the sequence of temporal frames, a new noise model replacing a current noise model, each noise model comprising several successive frames;
a step of searching for a noise transition between the new noise model and the current noise model; and
wherein,when the noise transition has been detected, the method comprises a step of updating the reference space as a function of the new noise model, the parameterizing step including a step of matching the parameters to the new noise model. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15)
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16. A system of voice recognition in a noise-ridden acoustic signal comprising:
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means to acquire the noise-ridden acoustic signal, digitize the noise-ridden acoustic signal and subdivide the noise-ridden acoustic signal into temporal frames;
a parametrization chain to translate the temporal frames containing speech into vectors of parameters in the frequency domain;
shape-recognition means with a reference space having references acquired during a learning stage, to compare the vectors of parameters coming from the parametrization chain with the references, so as to obtain recognition by the determination of a reference that most closely approaches the vectors of parameters;
means for modeling the noise to reiteratively prepare noise models, a new noise model replacing a current noise model;
means for detecting a noise transition between the new noise model and the current noise model;
means to match the parametrization chain with the new noise model having activated the noise transition; and
means to update the references of the reference space as a function of a noise level of the new noise model having activated the noise transition. - View Dependent Claims (17, 18, 19)
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Specification