Tandem MLNs Based Phonetic Feature Extraction for Phoneme Recognition
This paper presents a method for automatic phoneme recognition for Japanese language using tandem MLNs. Here, an accurate phoneme recognizer or phonetic type-writer, which extracts Out-Of-Vocabulary (OOV) word for resolving OOV problem that occurred when a new vocabulary does not exist in word lexicon, plays an important role in current Hidden Markov Model (HMM)-based Automatic Speech Recognition (ASR) system. From the experiments on Japanese Newspaper Article Sentences (JNAS) in clean acoustic environment, it is observed that the proposed method provides a higher phoneme correct rate and improves phoneme accuracy tremendously over the method based on a single MLN. Moreover, it requires fewer mixture components in HMMs. Consequently, less computation time is required for the HMMs.