In this paper, we described an effective speech
recognition algorithm that uses word trigram models and
a filled-pause procedure to reduce the memory and
computational requirements. With these methods, we can
perform execution in a 15M byte space for about a
1500-word vocabulary. Using this algorithm, a sentence
recognition rate of 66.7% was obtained for speaker
dependent recognition. We also described a procedure to
skip in speech data. Using this procedure, a
sentence recognition rate of 83.9% was obtained as
opposed to 0.0% with no such procedure for speaker
independent recognition. This procedure was then
extended to the procedure for dealing with filled-pauses
in spontaneous speech. In experiments for spontaneous
speech, we obtained a 42.0% sentence recognition rate;
including the semantically correct sentences, the
sentence recognition rate was about 75%. In the
future, we will try to raise the sentence recognition
rate for spontaneous speech by controlling the duration
of filled-pauses.
We would like to thank Dr. Yamazaki, President, ATR Interpreting Telecommunications Research Laboratories and Dr. Sagisaka for their continuous support of this work. We are also grateful to all of the members of Department 1 for their advice and encouragement.