Feature extraction and acoustic modeling: an approach for improved generalization across languages and accents

The paper proposes a solution that brings some advances to the genericity of the ASR technology towards tasks and languages. A non-linear discriminant model is built from multi-lingual, multi-task speech material in order to classify the acoustic signal into language independent phonetic units. Instead of considering this model for direct HMM state likelihood estimation, it rather operates as a first stage to produce discriminant features that can be further used in cascade with a traditional task/language specific ASR system. This first stage structure is expected to achieve a strong modeling of the cross-language variability of speech that can better handle pronunciation variations due for instance to regional and non-native accents. Moreover, the flexibility of this architecture still allow the development of small task/language dedicated ASR systems as a second stage structure, possibly with small amount of data. The benefit of this architecture is demonstrated through a fine analysis of modeling performance at the phoneme level and on two different isolated word recognition tasks featuring accent variabilities