Frame-Based Acoustic Feature Integration for Speech Understanding

Loïc Barrault (1) , Christophe Servan (1) , Driss Matrouf (1) , Georges Linarès (1) , Renato de Mori (1)

1 LIA - Laboratoire Informatique d'Avignon

With the purpose of improving Spoken Language Un- derstanding (SLU) performance, a combination of different acoustic speech recognition (ASR) systems is proposed. State a posteriori probabilities obtained with systems using different acoustic feature sets are combined with log-linear inter- polation. In order to perform a coherent combination of these probabilities, acoustic models must have the same topology (i.e. same set of states). For this purpose, a fast and efficient twin model training protocol is proposed. By a wise choice of acoustic feature sets and log-linear interpolation of their like- lihood ratios, a substantial Concept Error Rate (CER) reduction has been observed on the test part of the French MEDIA corpus.

Mots clés: speech recognition; posterior probabilities combination; speech understanding; frame based combination

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Loïc Barrault, Christophe Servan, Driss Matrouf, Georges Linarès, Renato de Mori. Frame-Based Acoustic Feature Integration for Speech Understanding. IEEE International Conference on Acoustics, Speech and Signal Processing, 2008. ICASSP 2008., Mar 2008, Las Vegas, NV,, United States. pp.4997-5000, ⟨10.1109/ICASSP.2008.4518780⟩. ⟨hal-00424663⟩