Multi-label Classification Models for Detection of Phonetic Features in building Acoustic Models

Abstract

Acoustic modeling in large vocabulary continuous speech recognition systems is commonly done by building the models for subword units such as phonemes, syllables or senones. In recent years, various end-to-end systems using acoustic models built at grapheme or phoneme level have also been explored. These systems either require a lot of data and/or heavily rely on the use of language models or pronunciation dictionary for good recognition performance. With the intention of reducing the dependence on data or external models, we have explored the usage of phonetic features in building acoustic models for speech recognition. The phonetic features describe a sound based on the speech production mechanism in humans. Multi-label classification models are built for detection of phonetic features in a given speech signal. The detected phonetic features are used along with the acoustic features as input to models for phoneme identification. The effectiveness of the proposed approach is demonstrated on TIMIT and Wall Street Journal corpora. Performance improvement over other phoneme recognition studies using the phonetic features is obtained.