Lombard speech synthesis using transfer learning in a Tacotron text-to-speech system

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Currently, there is increasing interest to use sequence-to-sequence models in text-to-speech (TTS) synthesis with attention like that in Tacotron models. These models are end-to-end, meaning that they learn both co-articulation and duration properties directly from text and speech. Since these models are entirely data-driven, they need large amounts of data to generate synthetic speech of good quality. However, in challenging speaking styles, such as Lombard speech, it is difficult to record sufficiently large speech corpora. Therefore, we propose a transfer learning method to adapt a TTS system of normal speaking style to Lombard style. We also experiment with a WaveNet vocoder along with a traditional vocoder (WORLD) in the synthesis of Lombard speech. The subjective and objective evaluation results indicated that the proposed adaptation system coupled with the WaveNet vocoder clearly outperformed the conventional deep neural network based TTS system in the synthesis of Lombard speech


Original languageEnglish
Title of host publicationProceedings of Interspeech
Publication statusPublished - 2019
MoE publication typeA4 Article in a conference publication
EventInterspeech - Graz, Austria
Duration: 15 Sep 201919 Sep 2019

Publication series

NameInterspeech - Annual Conference of the International Speech Communication Association
ISSN (Electronic)2308-457X


Internet address

    Research areas

  • Adaptation, Lombard speaking style, Tacotron, Text-To-Speech (TTS)

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