A Cantonese speech-driven talking face using translingual audio-to-visual conversion

This paper proposes a novel approach towards a video- realistic, speech-driven talking face for Cantonese. We present a technique that realizes a talking face for a target language (Cantonese) using only audio-visual facial recordings for a base language (English). Given a Cantonese speech input, we first use a Cantonese speech recognizer to generate a Cantonese syllable transcription. Then we map it to an English phoneme transcription via a translingual mapping scheme that involves symbol mapping and time alignment from Cantonese syllables to English phonemes. With the phoneme transcription, the input speech, and the audio-visual models for English, an EM-based conversion algorithm is adopted to generate mouth animation parameters associated with the input Cantonese audio. We have carried out audio-visual syllable recognition experiments to objectively evaluate the proposed talking face. Results show that the visual speech synthesized by the Cantonese talking face can effectively increase the accuracy of Cantonese syllable recognition under noisy acoustic conditions.