Speaker-Independent Phone Recognition Using BREF

Jean-Luc Gauvain, Lori Lamel

Abstract: A series of experiments on speaker-independent phone recognition of continuous speech have been carried out using the recently recorded BREF corpus. These experiments are the first to use this large corpus, and are meant to provide a baseline performance evaluation for vocabulary-independent phone recognition of French. The HMM-based recognizer was trained with hand-verified data from 43 speakers. Using 35 context-independent phone models, a baseline phone accuracy of 60% (no phone grammar) was obtained on an independent test set of 7635 phone segments from 19 new speakers. Including phone bigram probabilities as phonotactic constraints resulted in a performance of 63.5%. A phone accuracy of 68.6% was obtained with 428 context dependent models and the bigram phone language model. Vocabulary-independent word recognition results with no grammar are also reported for the same test data.