Error correction for speaker-independent isolated word recognition through likelihood compensation using phonetic bigram

We propose an error correction technique for speaker-independent isolated word recognition by compensating for a word´s likelihood. The likelihood is compensated for by the likelihood calculated by a phonetic bigram. The phonetic bigram is a phoneme model expressing the frame correlation within an utterance. A speaker-independent isolated word recognition experiment showed that our proposed technique reduces the recognition error compared to conventional techniques. The proposed technique achieves a performance almost equal to that without speaker adaptation compared to the conventional phoneme model adapted using several words