In the EMIME project, we developed a mobile device that performs personalized speech-to-speech translation such that a user’s spoken input in one language is used to produce spoken output in another language, while continuing to sound like the user’s voice. We integrated two techniques into a single architecture: unsupervised adaptation for HMM-based TTS using word-based large-vocabulary continuous speech recognition, and cross-lingual speaker adaptation (CLSA) for HMM-based TTS. The CLSA is based on a state-level transform mapping learned using minimum Kullback–Leibler divergence between pairs of HMM states in the input and output languages. Thus, an unsupervised cross-lingual speaker adaptation system was developed. End-to-end speech-to-speech translation systems for four languages (English, Finnish, Mandarin, and Japanese) were constructed within this framework. In this paper, the English-to-Japanese adaptation is evaluated. Listening tests demonstrate that adapted voices sound more similar to a target speaker than average voices and that differences between supervised and unsupervised cross-lingual speaker adaptation are small. Calculating the KLD state-mapping on only the first 10 mel-cepstral coefficients leads to huge savings in computational costs, without any detrimental effect on the quality of the synthetic speech.
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