Invited paper: Automatic speech recognition: History, methods and challenges
Pattern Recognition
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Handsets that are not seen in the training phase (unseen handsets) are significant sources of performance degradation for speaker identification (SID) applications in the telecommunication environment. In this paper, a novel latent prosody analysis (LPA) approach to automatically extract the most discriminative prosodic cues for assisting in conventional spectral feature-based SID is proposed. The concept of the LPA approach is to transform the SID problem into a full-text document retrieval-like task via 1) prosodic contour tokenization, 2) latent prosody analysis, and 3) speaker retrieval. Experimental results of the phonetically balanced, read-speech, handset-TIMIT (HTIMIT) database demonstrated that the proposed method of fusing the LPA prosodic feature-based SID systems with maximum-likelihood a priori handset knowledge interpolation (ML-AKI) spectral feature-based SID outperformed both the pitch and energy Gaussian mixture model (Pitch-GMM) and the bigram of the prosodic state (Bigram) counterparts for both cases of counting all and only unseen handsets.