Continuous speech recognition without end-point detection

A continuous speech recognition method that does not need explicit speech end-point detection is proposed. A one-pass decoding algorithm is modified to decode input speech of infinite length so that, with appropriate nonspeech models for silence and ambient noises, continuous speech recognition can be executed without explicit endpoint detection. The basic algorithm: 1) decodes a processing block of predetermined length, 2) traces back and finds the boundaries of the processing blocks where the word history in the preceding processing block is merged into one, and 3) restarts decoding from the boundary frame with the merged word history. The effectiveness of the method is verified by the two dictating experiments. With 100 consecutive sentences of utterances from a newspaper, the degradation of the recognition accuracy due to the modification of the decoder is about 5% compared with the results when the correct end-point is given. With a 30 minutes dialogue in a moving car, 75% correct and 69% accuracy score is obtained