  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.
