Speech segmentation without speech recognition
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
Phoneme segmentation of speech
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Thai speech processing technology: A review
Speech Communication
Using multiple acoustic feature sets for speech recognition
Speech Communication
Tone correctness improvement in speaker dependent HMM-based Thai speech synthesis
Speech Communication
State-dependent phoneme-based model merging for dialectal Chinese speech recognition
Speech Communication
Automatic Phonetic Segmentation by Score Predictive Model for the Corpora of Mandarin Singing Voices
IEEE Transactions on Audio, Speech, and Language Processing
Robust Recognition of Simultaneous Speech by a Mobile Robot
IEEE Transactions on Robotics
Hi-index | 12.05 |
In this paper, we investigate the application of a phoneme recognition system with a soft phoneme segmentation procedure for Thai speech. In addition, we propose a new method to classify the tonal accent of a syllable. The recognition system classifies Thai phonemes, including the 21-class initial consonants, the 18-class vowels, and the 9-class final consonants, using discrete hidden Markov models. Two features, i.e., the Mel frequency with perceptual linear prediction and the Mel frequency cepstrum coefficients, are compared to investigate their utilities in phoneme recognition. Neural networks are applied to classify the 5-class tonal accents by using the temporal variation of pitch frequencies across syllables as features. Speaker-dependent and speaker-independent data sets recorded from 30 speakers are used to test our recognition system. The experimental results show promising recognition performances for the phonemes and tonal accents in both data sets.