An Efficient Speech Recognition System for the Initials of Mandarin Syllables

Abstract

In a long-term research project, the recognition of Mandarin speech for very large vocabulary and unlimited text is considered. Its first stage goal is to recognize the Mandarin syllables. In a previous paper, an initial/final two-phase recognition approach to recognize these very confusing syllables was proposed, in which each syllable is divided into initial and final parts and recognized separately, and efficient recognition techniques for the finals were proposed and discussed. This paper serves as a continuation and proposes an efficient system to recognize the Mandarin initials. In this system, a classification procedure is first used to categorize the unknown initials into two groups C1 and C2; different approaches are then separately applied and independently optimized to recognize C1 and C2. It is found that Finite State Vector Quantization (FSVQ) is very useful, whose two modified versions, Modified FSVQ (MFSVQ) and the Second Order FSVQ (SOFSVQ), can provide the best recognition performance for C1 and C2 by carefully adjusting a design parameter called characteristic interval. Experimental results show that a recognition rate of 94.1% to 94.7% can be achieved using this system. Such a design is accomplished by carefully considering the special characteristics of Mandarin syllables and initials.