Bitstream-based feature extraction for wireless speech recognition

In this paper, we propose a feature extraction method for a speech recognizer that operates in digital communication networks. The feature parameters are basically extracted by converting the quantized spectral information of a speech coder into a cepstrum. We also combine the voiced/unvoiced information obtained from the bitstream of the speech coder into the recognition feature set. From speaker-independent connected digit HMM recognition, we find that the speech recognition system employing the proposed bitstream-based front-end gives superior word and string accuracies over a recognizer constructed from decoded speech signals. Its performance is comparable to that of the wireline recognition system that uses only the cepstrum as a feature set