8th European Conference on Speech Communication and Technology

Geneva, Switzerland
September 1-4, 2003


Learning Intra-Speaker Model Parameter Correlations from Many Short Speaker Segments

Anne K. Kienappel

Philips Research Laboratories, Germany

Very rapid speaker adaptation algorithms, such as eigenvoices or speaker clustering, typically rely on learning intra-speaker correlations of model parameters from the training data. On the base of this a-priori knowledge, many model parameters can be successfully adapted on the basis of few observations. However, eigenvoice training or speaker clustering is non-trivial with training databases containing many short speaker segments, where for each speaker the available data to detect intra-speaker correlations is sparse. We have trained eigenvoices that yield a small but significant word error rate reduction in on-line adaptation (i.e. self adaptation) for a telephony database with on average only 5 seconds of speech per speaker in training and test data.

Full Paper

Bibliographic reference.  Kienappel, Anne K. (2003): "Learning intra-speaker model parameter correlations from many short speaker segments", In EUROSPEECH-2003, 1473-1476.