Abstract
A study on acoustic-phonetic features for the obstruent detection and classification based on the knowledge of Mandarin speech is proposed. Seneff auditory model is used as the front-end processor for extracting acoustic-phonetic features. These features are rich in their information content in a hierarchical decision process to detect and classify the Mandarin obstruents. The preliminary experiments showed that accuracy of obstruent detection is about 84%. An algorithm based on the information of feature distribution is applied to further classify the obstruents into stops, fricatives, and affricates. The average accuracy is about 80%. The proposed approach based on the feature distribution is simple and effective. It could be a very promising method for searching acoustic-phonetic features for the phone recognition in continuous speech recognition.