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The advent of statistical speech synthesis has enabled the unification of the basic techniques used in speech synthesis and recognition. Adaptation techniques that have been successfully used in recognition systems can now be applied to synthesis systems to improve the quality of the synthesized speech. The application of vocal tract length normalization(More)
In the EMIME project we have studied un-supervised cross-lingual speaker adaptation. We have employed an HMM statistical framework for both speech recognition and synthesis which provides transformation mechanisms to adapt the synthesized voice in TTS (text-to-speech) using the recognized voice in ASR (automatic speech recognition). An important application(More)
Vocal tract length normalization (VTLN) has been successfully used in automatic speech recognition for improved performance. The same technique can be implemented in statistical parametric speech synthesis for rapid speaker adaptation during synthesis. This paper presents an efficient implementation of VTLN using expectation maximization and addresses the(More)
Vocal tract length normalization is an important feature nor-malization technique that can be used to perform speaker adaptation when very little adaptation data is available. It was shown earlier that VTLN can be applied to statistical speech synthesis and was shown to give additive improvements to CMLLR. This paper presents an EM optimization for(More)
Recent research has demonstrated the effectiveness of vocal tract length normalization (VTLN) as a rapid adaptation technique for statistical parametric speech synthesis. VTLN produces speech with naturalness preferable to that of MLLR-based adaptation techniques, being much closer in quality to that generated by the original average voice model. However(More)
This paper provides an overview of speaker adaptation research carried out in the EMIME speech-to-speech translation (S2ST) project. We focus on how speaker adaptation transforms can be learned from speech in one language and applied to the acoustic models of another language. The adaptation is transferred across languages and/or from recognition models to(More)
There has been increasing interest in the use of unsuper-vised adaptation for the personalisation of text-to-speech (TTS) voices, particularly in the context of speech-to-speech translation. This requires that we are able to generate adaptation transforms from the output of an automatic speech recognition (ASR) system. An approach that utilises unified ASR(More)
The EMIME project aims to build a personalized speech-to-speech translator, such that spoken input of a user in one language is used to produce spoken output that still sounds like the user's voice however in another language. This distinctiveness makes unsupervised cross-lingual speaker adaptation one key to the project's success. So far, research has been(More)
In this paper we present results of unsupervised cross-lingual speaker adaptation applied to text-to-speech synthesis. The application of our research is the personalisation of speech-to-speech translation in which we employ a HMM statistical framework for both speech recognition and synthesis. This framework provides a logical mechanism to adapt(More)