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Automatic speaker recognition algorithms typically characterize speech audiousing short-term spectral features that encode the physiological and anatomicalaspects of speech production. Such algorithms do not fully capitalize onspeaker-dependent characteristics present in behavioral speech features. Inthis work, we propose a prosody encoding network called DeepTalk for extractingvocal style features directly from raw audio data. The DeepTalk methodoutperforms several state-of-the-art speaker recognition systems acrossmultiple challenging datasets. The speaker recognition performance is furtherimproved by combining DeepTalk with a state-of-the-art physiological speechfeature-based speaker recognition system. We also integrate DeepTalk into acurrent state-of-the-art speech synthesizer to generate synthetic speech. Adetailed analysis of the synthetic speech shows that the DeepTalk captures F0contours essential for vocal style modeling. Furthermore, DeepTalk-basedsynthetic speech is shown to be almost indistinguishable from real speech inthe context of speaker recognition.

DeepTalk: Vocal Style Encoding for Speaker Recognition and Speech Synthesis