A novel path extension framework using steady segment detection for Mandarin speech recognition

Frame based decoders are short of using long span of time knowledge while segment based decoders often confuse with complex calculating. This paper proposes a novel decoding framework by integrating steady speech segments information into path extension procedure. Firstly, as baseline decoding system, a dynamic lexicon-tree copy recognizer is developed, which aims to accelerate popular frame based recognizer, HTK. Steady segments, where the spectrum is stable, are extracted using landmark detection, and then detection results are provided to the following decoding module. At decoding stage, traditional inter-HMM token spreading framework is modified using steady segment knowledge, based on the observation that coexistence of steady frame and inter-HMM extension is impossible. Experiments conducted on Mandarin broadcasting speech show that the character error rate and run time achieve 22.1% and 5.24% relative reduction respectively.