Speaker: Florian Metze, UKA-ISL

Titel: Speech Recognition using Discriminative Combination of Articulatory Feature Detectors

Abstract:
"Articulatory Features" such as "VOICED" and "NASAL" are commonly used to describe speech 
production and the resulting speech sounds. For notational purposes, typical combinations 
of these features are sompacted int so-called "phones", if they also help lexical disambiguation. 
The phonetic transcriptions of words are tabulated in dictionaries which form the basis of 
most of today's state-of-the-art HMM-based speech recognition systems.

As phones are more of a convenient short-hand notation for the description of speech then 
an inherent property of speech, there is interest in removing them from the statistical 
modeling process used for automatic speech recognition in order to improve speech-to-text 
performance. Approaches relying on articulatory properties alone however have a computational 
complexity rendering them unsuitable for mainstream applications.

This talk will present a stream architecture which allows to use both standard phone- and novel 
articulatory feature-models for speech recognition, it will demonstrate how articulatory 
features are better suited to model human speech and present recent advances in the discriminative 
training of context-dependent articulatory feature detectors to improve speech recognition.