4th International Conference on Spoken Language Processing

Philadelphia, PA, USA
October 3-6, 1996

Hidden Markov Models Merging Acoustic and Articulatory Information to Automatic Speech Recognition

Bruno Jacob, Christine Senac

IRIT- CNRS UMR 5055 - Université Paul Sabatier, Toulouse, France

This paper describes a new scheme for robust speech recognition systems where visual information and acoustic features are merged. Using as robust unit the « pseudo-diphone », we compare a global Hidden Markov Model (HMM) and a Master/Slave HMM through a centisecond preprocessing and through a segmental one. We confirm by experimentation the importance of articulatory features in clean and noisy environments.

Full Paper

Bibliographic reference.  Jacob, Bruno / Senac, Christine (1996): "Hidden Markov models merging acoustic and articulatory information to automatic speech recognition", In ICSLP-1996, 2313-2315.