Lombard speech synthesis using transfer learning in a Tacotron text-to-speech system
Loading...
Access rights
openAccess
publishedVersion
URL
Journal Title
Journal ISSN
Volume Title
A4 Artikkeli konferenssijulkaisussa
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Date
Major/Subject
Mcode
Degree programme
Language
en
Pages
Series
Proceedings of Interspeech, pp. 2833-2837, Interspeech - Annual Conference of the International Speech Communication Association
Abstract
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 speechDescription
Other note
Citation
Bollepalli, B, Juvela, L & Alku, P 2019, Lombard speech synthesis using transfer learning in a Tacotron text-to-speech system. in Proceedings of Interspeech. Interspeech - Annual Conference of the International Speech Communication Association, International Speech Communication Association (ISCA), pp. 2833-2837, Interspeech, Graz, Austria, 15/09/2019. https://doi.org/10.21437/Interspeech.2019-1333