This paper proposes a speech recognition algorithm for large vocabulary continuous speech. The proposed algorithm is based on the hidden Markov model (HMM)-LR algorithm using a generalized predictive LR parser and phoneme HMMs. The following three techniques are applied to improve recognition performance and reduce processing time. 1 The forward and the backward likelihood are used to accurately determine the likelihood in the beam search. 2 To reduce the trellis computation in HMM speech recognition and for efficient search, only the speech frames in which the predicted phoneme seems to exist are used by the window for phoneme matching. 3 For efficient search, adjusting identical phoneme sequences are merged by checking the stack and the state of the LR parser. The algorithm was applied to a telephone directory assistance task involving more than 70, 000 subscribers. A recognition experiment for continuous word utterance was done. The sentence recognition rate was 85 percent for speaker-dependent speech recognition; the sentence recognition rate was 71 percent for speaker-independent speech recognition. The sentence understanding rate was 59 percent for speaker-dependent speech recognition with spontaneous utterances.