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—A statistical parametric approach to speech synthesis based on hidden Markov models (HMMs) has grown in popularity over the last few years. In this approach, spectrum, excitation, and duration of speech are simultaneously modeled by context-dependent HMMs, and speech waveforms are generated from the HMMs themselves. Since December 2002, we have publicly(More)
In conventional speech synthesis, large amounts of phonetically balanced speech data recorded in highly controlled recording studio environments are typically required to build a voice. Although using such data is a straightforward solution for high quality synthesis, the number of voices available will always be limited, because recording costs are high.(More)
Our recent experiments with HMM-based speech synthesis systems have demonstrated that speaker-adaptive HMM-based speech synthesis (which uses an 'average voice model' plus model adaptation) is robust to non-ideal speech data that are recorded under various conditions and with varying microphones , that are not perfectly clean, and/or that lack of pho-netic(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)
We describe a hidden Markov model (HMM)-based speech synthesis system developed at the Nagoya Institute of Technology (NIT) for Blizzard Challenge 2009. We incorporated several state-of-the-art technologies into this system, including the Speech Transformation and Representation using Adaptive Interpolation of weiGHTed spectrum (STRAIGHT) vocoder, minimum(More)
In hidden Markov models (HMMs), state duration probabilities decrease exponentially with time. It would be an inappropriate representation of temporal structure of speech. One of the solutions for this problem is integrating state duration probability distributions explicitly into the HMM. This form is known as a hidden semi-Markov model (HSMM). Although a(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)