Unsupervised adaptation for HMM-based speech synthesis
نویسندگان
چکیده
It is now possible to synthesise speech using HMMswith a comparable quality to unit-selection techniques. Generating speech from a model has many potential advantages over concatenating waveforms. The most exciting is model adaptation. It has been shown that supervised speaker adaptation can yield highquality synthetic voices with an order of magnitude less data than required to train a speaker-dependent model or to build a basic unit-selection system. Such supervised methods require labelled adaptation data for the target speaker. In this paper, we introduce a method capable of unsupervised adaptation, using only speech from the target speaker without any labelling.
منابع مشابه
Two-pass decision tree construction for unsupervised adaptation of HMM-based synthesis models
Hidden Markov model (HMM) -based speech synthesis systems possess several advantages over concatenative synthesis systems. One such advantage is the relative ease with which HMM-based systems are adapted to speakers not present in the training dataset. Speaker adaptation methods used in the field of HMM-based automatic speech recognition (ASR) are adopted for this task. In the case of unsupervi...
متن کاملExplorer Unsupervised cross - lingual speaker adaptation for HMM - based speech synthesis
In the EMIME project, we are developing 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 integrate two techniques, unsupervised adaptation for HMM-based TTS using a wordbased large-vocabulary continuous speech recognizer...
متن کاملSome Aspects of ASR Transcription Based Unsupervised Speaker Adaptation for HMM Speech Synthesis
Statistical parametric synthesis offers numerous techniques to create new voices. Speaker adaptation is one of the most exciting ones. However, it still requires high quality audio data with low signal to noise ration and precise labeling. This paper presents an automatic speech recognition based unsupervised adaptation method for Hidden Markov Model (HMM) speech synthesis and its quality evalu...
متن کاملAnalysis of unsupervised cross-lingual speaker adaptation for HMM-based speech synthesis using KLD-based transform mapping
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 contin...
متن کاملPersonalising speech-to-speech translation: Unsupervised cross-lingual speaker adaptation for HMM-based speech synthesis
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 synthesised speech output to the voice of the us...
متن کاملThe Effects of Phoneme Errors in Speaker Adaptation for HMM Speech Synthesis
In this paper the phoneme errors in adaptation data of HMM based synthesis is investigated. Phoneme errors are likely to appear in automatic speech recognition (ASR) based transcriptions. The research also investigates the perspective of merely ASR transcription based unsupervised adaptation. To achieve better quality a new method is introduced for selecting an optimal subset of ASR transcripti...
متن کامل