Modeling Lexical Tones for Speaker Discrimination

Fundamental frequency (F0) has been widely studied and used in the context of speaker discrimination and forensic voice comparison casework, but most previous studies focused on long-term F0 statistics. Lexical tone, the linguistically structured and dynamic aspects of F0, has received much less research attention. A main methodological issue lies on how tonal F0 should be parameterized for the best speaker discrimination performance. This paper compares the speaker discriminatory performance of three approaches with lexical tone modeling: discrete cosine transform (DCT), polynomial curve fitting, and quantitative target approximation (qTA). Results show that using parameters based on DCT and polynomials led to similarly promising performance, whereas those based on qTA generally yielded relatively poor performance. Implications modeling surface tonal F0 and the underlying articulatory processes for speaker discrimination are discussed.