Rapid unsupervised adaptation to children´s speech on a connected-digit task

We are exploring ways in which to rapidly adapt our neural network classifiers to new speakers and conditions using very small amounts of speech, say, one or a few words. Our approach is to perform a speaker-dependent warping of the frequency scale by selecting a Bark offset for each speaker. We choose the offset for a speaker to be the one that maximizes our recognizer output score on the adaptation utterance. We then use the speaker´s offset during evaluation of all other utterances by the speaker. To test our approach, we evaluate an adult-speech trained recognizer on children´s speech from the same task both before and after adaptation to each child´s voice. Using only a single digit for adaptation, we have reduced the word error rate for children´s speech from 9.6% to 4.2%. Using a seven-digit utterance further reduced the error rate to 3.5%