Scalable architecture of tone classification function for tonal speech recognizer

Tone classification function is used for improving recognition accuracy in tonal speech recognizer (TONE-SPEC). Although average magnitude difference function (AMDF) is generally used to find pitch period of fundamental frequency, there are many frame-repeated processes. This paper proposes scalable architecture of tone classification function for tonal speech recognizer. In the proposed architecture, the number of frames is reduced using vowel-AMDF (V-AMDF). Moreover, there is no frame iteration because the architecture converts series computation of conventional tone classification function into parallel. The parallel computation is designed to be able to reduce or extend the number of frame. Our architecture is set and evaluated with 10 Thai words selected from TV remote control commands and the words having the same phoneme but different tones. The experimental results show that the time consuming of general AMDF and series V-AMDF are improved 85.2% and 72.7%, respectively.