Discriminative Training for Neural Predictive Coding Applied to Speech Features Extraction
نویسنده
چکیده
In this paper, we present a predictive neural network called Neural Predictive Coding (NPC). This model is used for non linear discriminant features extraction (DFE) applied to phoneme recognition. We validate the nonlinear prediction improvement of the NPC model. We also, present a new extension of the NPC model : NPC-3. In order to evaluate the performances of the NPC-3 model, we carried out a study of Darpa-Timit phonemes (in particular /b/, /d/, /g/ and /p/, /t/, /q/ phonemes) recognition. Comparisons with traditionnal coding methods are presented: they put in obsviousness an improvement of the classification. We also show how an adaptative constraint allows improvements on recognition task.
منابع مشابه
Neural predictive coding for speech discriminant feature extraction: The DFE-NPC
In this paper, we present a predictive neural network called Neural Predictive Coding (NPC). This model is used for non linear discriminant features extraction (DFE) applied to phoneme recognition. We also, present a new extension of the NPC model : DFE-NPC. In order to evaluate the performances of the DFE-NPC model, we carried out a study of Darpa-Timit phonemes (in particular /b/, /d/, /g/ an...
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