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Improving the recognition of pathological voice using the discriminant HLDA transformation

Domaine:

natural language processinghealthcare

Type de record:

paperdataset
Créateur:
LacDi IbnHam
Éditeur:
AnaEcoIns
Éditeur:
CCSD
Hôte:avatar
International audience 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.

Visit

inria.hal.science

Tasks

automatic speech recognitionspeech processing

Tags

MFCCGMMHLDAPathological voicesHTKHMMAutomatic Speech Recognition (ASR)[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing

Licenses

https://about.hal.science/hal-authorisation-v1/info:eu-repo/semantics/OpenAccess

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