Language Identification Using Pitch Contour Information in the Ergodic Markov Model

It had been shown that a segment of pitch contour represented by a set of Legendre polynomial coefficients was successful to the pair-wise language identification task. Feature vectors comprising these polynomial coefficients were formerly modeled by a Gaussian mixture model (GMM) for each language. However, the static model like GMM does not take advantage of the temporal information across several pitch contours. It is intuitive that the temporal information of prosodic features should be used for capturing the characteristics of a specific language. In this paper, a novel dynamic model in ergodic topology is proposed. The experiments show that the proposed method significantly improves the identification rate, even for stress-timed and syllable-timed languages.