The paper presents a mechanism to perform speaker adaptation in speech synthesis based on deep neural networks (DNNs). The mechanism extracts speaker identification vectors, socalled d-vectors, from the training speakers and uses them jointly with the linguistic features to train a multi-speaker DNNbased text-to-speech synthesizer (DNN-TTS). The d-vectors are derived by applying principal component analysis (PCA) on the bottleneck features of a speaker classifier network. At the adaptation stage, three variants are explored: (1) d-vectors calculated using data from the target speaker, or (2) d-vectors calculated as a weighted sum of d-vectors from training speakers, or (3) d-vectors calculated as an average of the above two approaches. The proposed method of unsupervised adaptation using the d-vector is compared with the commonly used i-vector based approach for speaker adaptation. Listening tests show that: (1) for speech quality, the d-vector based approach is significantly preferred over the i-vector based approach. All the d-vector variants perform similar for speech quality; (2) for speaker similarity, both d-vector and i-vector based adaptation were found to perform similar, except a small significant preference for the d-vector calculated as an average over the i-vector.
In this paper we introduce a new cepstral coefficient extraction method based on an intelligibility measure for speech in noise, the Glimpse Proportion measure. This new method aims to increase the intelligibility of speech in noise by modifying the clean speech, and has applications in scenarios such as public announcement and car navigation systems. We first explain how the Glimpse Proportion measure operates and further show how we approximated it to integrate it into an existing spectral envelope parameter extraction method commonly used in the HMM-based speech synthesis framework. We then demonstrate how this new method changes the modelled spectrum according to the characteristics of the noise and show results for a listening test with vocoded and HMM-based synthetic speech. The test indicates that the proposed method can significantly improve intelligibility of synthetic speech in speech shaped noise.Index Terms-cepstral coefficient extraction, objective measure for speech intelligibility, Lombard speech, HMM-based speech synthesis
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