Discriminative training of high performance speech recognizer using N best candidates

Proposes an N-best candidates based, discriminative training procedure for constructing high performance HMM speech recognizers. The algorithm has two features: (1) a new frame-level loss function; (2) N best candidates are used for training. The new frame-level loss function, defined as a rectified log likelihood difference between the correct and other competing hypotheses, is minimized over all training utterances. Two speech recognition applications have been tested: speaker independent, small vocabulary (10 Mandarin Chinese digits), continuous speech recognition; and a speaker-trained, large vocabulary (5,000 commonly used Chinese words), isolated word recognition. Significant performance improvement over the traditional maximum likelihood trained HMMs has been obtained. In the connected Chinese digit recognition experiment, the string error rate is reduced from 17% to 10.8% for unknown length decoding and from 8.2% to 5.2% for known length decoding. In the large vocabulary, isolated word recognition experiment, the recognition error rate is improved from 6.8% to 3.8%