Evaluation of Tree-Trellis Based Decoding in Over-Million LVCSR

Very large vocabulary continuous speech recognition (CSR) that can recognize every sentence is one of important goals in speech recognition. Several attempts have been made to achieve very large vocabulary CSR. However, very large vocabulary CSR using a tree-trellis based decoder has not been reported. We report the performance evaluation and improvement of the “Julius” tree-trellis based decoder in large vocabulary CSR (LVCSR) involving more than one million vocabulary, referred to here as over-million LVCSR. Experiments indicated that Julius achieved a word accuracy of about 91% and a real time factor of about 2 in over-million LVCSR for Japanese newspaper speech transcription.