ASRU 2013

Unsupervised Acoustic Model Training with Limited Linguistic Resources

Lori Lamel (CNRS-LIMSI)

This talk will summarize our experience in developing speech-to-text transcription systems with little or no manually transcribed data and limited resources (lexicon or language specific knowledge). Since our initial studies in 2000, we have applied such approaches to a number of languages, including Finnish, Ukranian, Portuguese, Bulgarian, Hungarian, Slovak and Latvian. We have found significant improvements using acoustic features produced by discriminative classifiers such as multi-layer perceptrons (MLPs) trained for other languages. On the language modeling side we have explored unsupervised morphological decomposition to reduce the need for textual resources. These studies have been carried out in collaboration with colleagues at LIMSI ( and Vocapia Research (, most recently in the context of the Quaero program (


Lori Lamel joined the CNRS-LIMSI laboratory in October 1991 where she is now senior research scientist (DR1). She obtained her Ph.D. degree in EECS from MIT in 1988 and has over 270 reviewed publications. Her research centers on speaker-independent, large vocabulary continuous speech recognition; studies in acoustic-phonetics; lexical and phonological modeling; design, analysis, and realization of large speech corpora; speaker and language identification. One focus has been on the rapid development of speech-to-text transcription systems via cross-lingual porting and unsupervised acoustic model training.