Automatic language analysis and identification based on speech production knowledge
Abhijeet Sangwan, Mahnoosh Mehrabani, John H. L. Hansen · 2010
In this paper, a language analysis and classification system that leverages knowledge of speech production is proposed. The proposed scheme automatically extracts key production traits (or “hot-spots”) that are strongly tied to the underlying language structure. Particularly, the speech utterance is first parsed into consonant and vowel clusters. Subsequently, the production traits for each cluster is represented by the corresponding temporal evolution of speech articulatory states. It is hypothesized that a selection of these production traits are strongly tied to the underlying language, and can be exploited for language ID. The new scheme is evaluated on our South Indian Languages (SInL) corpus which consists of 5 closely related languages spoken in India, namely, Kannada, Tamil, Telegu, Malayalam, and Marathi. Good accuracy is achieved with a rate of 65% obtained in a difficult 5-way classification task with about 4sec of train and test speech data per utterance. Furthermore, the proposed scheme is also able to automatically identify key production traits of each language (e.g., dominant vowels, stop-consonants, fricatives etc.).