4th International Conference on Spoken Language Processing
Philadelphia, PA, USA
One of the problems of the speaker-independent continuous speech recognition systems is their inability to cope with the inter-speaker variability. When we find test speakers with different characteristics from the ones presented in the training pool we observe a large degradation on the system performance. To overcome this problem speaker-adaptation techniques may be used to provide near speaker-dependent accuracy. In this work we present a speaker-adaptation technique applied to a hybrid HMM-MLP system for large vocabulary, continuous speech recognition. This technique is based on an architecture that employs a trainable Linear Input Network (LIN) to map the speaker specific features input vectors to the speaker-independent system. This speaker-adaptation technique will be evaluatedin an incremental speaker-adaptation task using the Wall Street Journal (WSJ) database. Both supervised and unsupervised modes are evaluated. The results show that speaker-adaptation within the hybrid framework can substantially improve system performance.
Bibliographic reference. Neto, Joao P. / Martins, Ciro A. / Almeida, Luís B. (1996): "An incremental speaker-adaptation technique for hybrid HMM-MLP recognizer", In ICSLP-1996, 1293-1296.