Improving the recognition of pathological voice using the discriminant HLDA transformation

Abstract : In this paper, we propose a simple and fast method for evaluating the pathological voice (esophageal) by applying the continuous speech recognition in a speaker dependent mode, on our own database of the pathological voice, we call FPSD (French Pathological Speech Database). The recognition system used is implemented using the HTK platform, based on HMM/GMM monophone models. The acoustic vectors are linearly transformed by the HLDA (Heteroscedastic Linear Discriminant Analysis) method to reduce their size in a smaller space with good discriminative properties. The obtained phone recognition rate (63.59 %) is very promising when we know that esophageal voice contains unnatural sounds, difficult to understand.
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Submitted on : Wednesday, December 10, 2014 - 2:53:41 PM
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Othman Lachhab, Joseph Di Martino, El Hassane Ibn Elhaj, Ahmed Hammouch. Improving the recognition of pathological voice using the discriminant HLDA transformation. 3rd International IEEE Colloquium on Information Science and Technology, Oct 2014, Tetuan-Chefchaouen, Morocco. ⟨hal-01093309⟩

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