EUROSPEECH 2001 Scandinavia
7th European Conference on Speech Communication and Technology
2nd INTERSPEECH Event

Aalborg, Denmark
September 3-7, 2001

                 

Improving Genericity for Task-Independent Speech Recognition

Fabrice Lefevre, Jean-Luc Gauvain, Lori Lamel

LIMSI-CNRS, France

Although there have been regular improvements in speech recognition technology over the past decade, speech recognition is far from being a solved problem. Recognition systems are usually tuned to a particular task and porting the system to a new task (or language) is both timeconsuming and expensive. In this paper, issues in speech recognizer portability are addressed through the development of generic core speech recognition technology. First, the genericity of wide domain models is assessed by evaluating performance on several tasks. Then, the use of transparent methods for adapting generic models to a specific taskis explored. Finally, further techniques are evaluated aiming at enhancing the genericity of the wide domain models. We show that unsupervised acoustic model adaptation and multi-source training can reduce the performance gap between task-independent and task-dependent acoustic models, and for some tasks even out-perform task-dependent acoustic models.

Full Paper

Bibliographic reference.  Lefevre, Fabrice / Gauvain, Jean-Luc / Lamel, Lori (2001): "Improving genericity for task-independent speech recognition", In EUROSPEECH-2001, 1241-1244.