Shared speech attribute augmentation for English-Tibetan cross-language phone recognition

There has been a challenging research topic on exploring an universal set of speech attributes sharing among a large number of languages for detection-based bottom-up cross-language speech recognition. In some recent research works, articulatory features are used as an universal set of speech attributes shared across many different languages. Since they are defined by human as a set of semantic articulatory descriptions of phones, these manually specified attributes suffer from the incomplete capturing articulation information of all languages and are not distinctive enough for accurate phoneme recognition for cross-language transfer. In this paper, we are solving the problem of a more complete set of articulatory features representation by sparse coding method. We learned the augmented articulatory attributes which sparsely represent more speech articulation information sharing between source and target language. The augmented attributes performed the better accuracy over semantic attributes in our experiments for English-Tibetan cross-language phone recognition.