Continuous speech recognition without end-point detection

A new continuous speech recognition method that does not need the explicit speech end-point detection is proposed. A one-pass decoding algorithm is modified to decode the input speech of infinite length so that, with appropriate nonspeech models for silence and ambient noises, continuous speech recognition can be executed without the explicit endpoint detection. The basic algorithm is 1) decode a processing block of the predetermined length, 2) traceback and find the boundaries of the processing blocks where the word history in the preceding processing block is merged into one, and 3) restart decoding from the boundary frame with the merged word history. The effectiveness of the method is verified by the two dictating experiments. With consecutive 100 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.