149 research outputs found
Adversarially Trained Autoencoders for Parallel-Data-Free Voice Conversion
We present a method for converting the voices between a set of speakers. Our
method is based on training multiple autoencoder paths, where there is a single
speaker-independent encoder and multiple speaker-dependent decoders. The
autoencoders are trained with an addition of an adversarial loss which is
provided by an auxiliary classifier in order to guide the output of the encoder
to be speaker independent. The training of the model is unsupervised in the
sense that it does not require collecting the same utterances from the speakers
nor does it require time aligning over phonemes. Due to the use of a single
encoder, our method can generalize to converting the voice of out-of-training
speakers to speakers in the training dataset. We present subjective tests
corroborating the performance of our method
Collapsed speech segment detection and suppression for WaveNet vocoder
In this paper, we propose a technique to alleviate the quality degradation
caused by collapsed speech segments sometimes generated by the WaveNet vocoder.
The effectiveness of the WaveNet vocoder for generating natural speech from
acoustic features has been proved in recent works. However, it sometimes
generates very noisy speech with collapsed speech segments when only a limited
amount of training data is available or significant acoustic mismatches exist
between the training and testing data. Such a limitation on the corpus and
limited ability of the model can easily occur in some speech generation
applications, such as voice conversion and speech enhancement. To address this
problem, we propose a technique to automatically detect collapsed speech
segments. Moreover, to refine the detected segments, we also propose a waveform
generation technique for WaveNet using a linear predictive coding constraint.
Verification and subjective tests are conducted to investigate the
effectiveness of the proposed techniques. The verification results indicate
that the detection technique can detect most collapsed segments. The subjective
evaluations of voice conversion demonstrate that the generation technique
significantly improves the speech quality while maintaining the same speaker
similarity.Comment: 5 pages, 6 figures. Proc. Interspeech, 201
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