5th European Conference on Speech Communication and Technology

Rhodes, Greece
September 22-25, 1997

Automatic Selection of Segmental Acoustic Parameters by means of Neural-Fuzzy Networks For Reordering the N-Best HMM Hypotheses

Thierry Moudenc, Guy Mercier

France- Telecom - CNET/DIH/RCP, Lannion, France

We present a neural fuzzy network architecture devoted to the recognition of specific segmental phonetic features.. A neural fuzzy network allows us to select the best acoustic parameters associated with eachfeature and to compute an phonetic segmental plausibility score. Segments result from the alignements provided by an allophone based Markov model. These segmental scores are then processed by a statistical post-processing system for reordering the N-best HMM hypotheses. This post-processing is based on the computation of segmental scores for each solution under the hypotheses of a correct solution and of an incorrect solution. Moreover, we present comparison results between these neural fuzzy network architecture and a classical one, on 3 speaker-independent telephone databases.

Full Paper

Bibliographic reference.  Moudenc, Thierry / Mercier, Guy (1997): "Automatic selection of segmental acoustic parameters by means of neural-fuzzy networks for reordering the n-best HMM hypotheses", In EUROSPEECH-1997, 83-86.