Multilingual articulatory features augmentation learning

摘要

Articulatory features are used as an universal set of speech attributes shared across many different languages. Some multilingual and cross-language speech recognition systems using articulatory features have been shown to improve the performance. The existing articulatory features are defined by phonetician as a set of articulatory descriptions of phones, which represent some semantic information explaining how humans produce speech sounds via the interaction of different physiological structures. But these manually specified attributes suffer from the incomplete capturing articulation information of all languages and are not distinctive enough for accurate monolingual and multilingual phoneme recognition. In this paper, we are solving the problem of a more complete set of articulatory features representation by sparse coding methods. We learned the latent attributes that sparsely represent more speech articulation information sharing between English and Tibetan languages. Models based on the concatenated semantic and latent speech attributes performed the better accuracy over the existing methods in our experiments for English-Tibetan bilingual phone recognition.