Waveform generation based on signal reshaping for statistical parametric speech synthesis

Felipe Espic, Cassia Valentini Botinhao, Zhizheng Wu, Simon King

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We propose a new paradigm of waveform generation for Statistical Parametric Speech Synthesis that is based on neither source-filter separation nor sinusoidal modelling. We suggest that one of the main problems of current vocoding techniques is that they perform an extreme decomposition of the speech signal into source and filter, which is an underlying cause of “buzziness”, “musical artifacts”, or “ muffled sound” in the synthetic speech. The proposed method avoids making unnecessary assumptions and decompositions as far as possible, and uses only the spectral envelope and F0 as parameters. Prerecorded speech is used as a base signal, which is “reshaped” to match the acoustic specification predicted by the statistical model, without any source-filter decomposition. A detailed description of the method is presented, including implementation details and adjustments. Subjective listening test evaluations of complete DNN-based text-to-speech systems were conducted for two voices: one female and one male. The results show that the proposed method tends to outperform the state-of-theart standard vocoder STRAIGHT, whilst using fewer acoustic parameters.
Original languageEnglish
Title of host publicationInterspeech 2016
Place of PublicationSan Francisco, United States
Number of pages5
Publication statusPublished - 12 Sep 2016
EventInterspeech 2016 - San Francisco, United States
Duration: 8 Sep 201612 Sep 2016

Publication series

PublisherInternational Speech Communication Association
ISSN (Print)1990-9772


ConferenceInterspeech 2016
Country/TerritoryUnited States
CitySan Francisco
Internet address


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