A NEW CONNECTED WORD RECOGNITION USING SYNERGIC HMM AND DTW

Connected Word Recognition (CWR) is used in many applications such as voice-dialing telephone, automatic data entry, automated banking systems and, etc. This paper presents a novel architecture for CWR based on synergic Hidden Markov Model (HMM) and Dynamic Time Warping (DTW). At first, the proposed architecture eliminates obvious silent times from inputted speech utterance by preprocessing operations. Then, in order to determine boundaries of the existing words in the compressed utterance, a set of candidates for boundary of each word is computed by using the existing capability of the HMM model. Finally, recognition operation is performed by using the synergic between HMM and DTW methods. The architecture has been compared with TLDP method from recognition accuracy and time complexity viewpoints. The evaluation results show that the proposed method significantly improves recognition accuracy and recognition time in comparison with the TLDP method.