Self-adaptation using eigenvoices for large-vocabulary continuous speech recognition

In this paper, we present the application of eigenvoices to self-adaptation. This adaptation algorithm happens to be rather well-suited for such a task. First, it is an extremely fast adaptation algorithm, and thus well tailored to work for very short amounts of adaptation data. It is also believed to be rather more tolerant of errorful recognition. A third property is the explicit aim to reduce the dimensionality that translates into compact computation of the likelihood. This can be ex-ploited as an embedded confidence measure to minimize the impact of errors in the transcription. Our experiments were carried out on the Wall Street Journal evaluation task (WSJ). We reduced our word error rate (WER) by one percent absolute to 9.7%.