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We propose a Kullback-Leibler divergence (KLD) and deep neural net (DNN) based approach to cross-lingual TTS (CL-TTS) training. A speaker independent DNN (SI-DNN) ASR is used to equalize the speaker difference between a source speaker in L1 and a reference speaker in L2. Two speaker dependent GMM-HMM parametric TTS systems are first trained in the respective languages. The senones sets of the two...
In voice conversion task, prosody conversion especially pitch conversion is a very challenging research topic because of the discontinuity property of pitch. Conventionally pitch conversion is always achieved by adjusting the mean and variance of the source pitch distribution to the target pitch distribution. This method removes most of the detailed information of the speaker's prosody and only maintains...
In HMM-based speech synthesis, context-dependent hidden Markov model (HMM) is widely used for its capability to synthesize highly intelligible and fairly smooth speech. However, to train HMMs of all possible contexts well is difficult, or even impossible, due to the intrinsic, insufficient training data coverage problem. As a result, thus trained models may over fit and their capability in predicting...
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