Phoneme recognition expert system using spectrogram reading knowledge and neural networks

Abstract

AbstractA human expert spectrogram reader is able to recognize phonemes in high‐accuracy performing phoneme segmentation and phoneme identification simultaneously using one's spectrogram reading knowledge. Spectrogram reading knowledge consists mainly of two parts: a strategic part for phoneme segmentation and identification and a pattern‐matching part. There are several knowledge‐based approaches in which all knowledge is implemented as rules. However, it is especially difficult to describe the pattern matching part of the knowledge as rules and to extract acoustic features automatically for phoneme identification. Here, we construct a phoneme recognition expert system which consists of two parts: (1) rule‐based phoneme segmentation, and (2) neural network‐based phoneme identification for knowledge such as pattern matching. This paper presents the architecture of the phoneme recognition expert system with its experimental result tested on Japanese consonants. The experimental result shows that 90.8 percent of the phonemes were segmented correctly and 92.4 percent of the phonemes were identified correctly within the correct segments, which means 83.9 percent of the phonemes were correctly recognized both in segmentation and identification.