Controlling Prosody in End-to-End TTS: A Case Study on Contrastive Focus Generation

Paper


Latif, Siddique, Kim, Inyoung, Calapodescu, Ioan and Besacier, Laurent. 2021. "Controlling Prosody in End-to-End TTS: A Case Study on Contrastive Focus Generation." 25th Conference on Computational Natural Language Learning (CoNLL 2021). Punta Cana, Dominican Republic 10 - 11 Nov 2021 Stroudsburg, Pennsylvania. https://doi.org/10.18653/v1/2021.conll-1.42
Paper/Presentation Title

Controlling Prosody in End-to-End TTS: A Case Study on Contrastive Focus Generation

Presentation TypePaper
AuthorsLatif, Siddique (Author), Kim, Inyoung (Author), Calapodescu, Ioan (Author) and Besacier, Laurent (Author)
Journal or Proceedings TitleProceedings of the 25th Conference on Computational Natural Language Learning (CoNLL 2021)
ERA Conference ID42652
Number of Pages8
Year2021
Place of PublicationStroudsburg, Pennsylvania
ISBN9781955917056
Digital Object Identifier (DOI)https://doi.org/10.18653/v1/2021.conll-1.42
Web Address (URL) of Paperhttps://aclanthology.org/2021.conll-1.42/
Conference/Event25th Conference on Computational Natural Language Learning (CoNLL 2021)
Conference on Natural Language Learning
Event Details
Conference on Natural Language Learning
CoNLL
Rank
A
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Event Details
25th Conference on Computational Natural Language Learning (CoNLL 2021)
Event Date
10 to end of 11 Nov 2021
Event Location
Punta Cana, Dominican Republic
Abstract

While End-2-End Text-to-Speech (TTS) has made significant progresses over the past few years, these systems still lack intuitive user controls over prosody. For instance, generating speech with fine-grained prosody control (prosodic prominence, contextually appropriate emotions) is still an open challenge. In this paper, we investigate whether we can control prosody directly from the input text, in order to code information related to contrastive focus which emphasizes a specific word that is contrary to the presuppositions of the interlocutor. We build and share a specific dataset for this purpose and show that it allows to train a TTS system were this fine-grained prosodic feature can be correctly conveyed using control tokens. Our evaluation compares synthetic and natural utterances and shows that prosodic patterns of contrastive focus (variations of Fo, Intensity and Duration) can be learnt accurately. Such a milestone is important to allow, for example, smart speakers to be programmatically controlled in terms of output prosody.

KeywordsEnd-to-End TTS, fine-grained prosody control, contrastive focus, interrogative/assertive sentences
ANZSRC Field of Research 2020460211. Speech production
460208. Natural language processing
461104. Neural networks
461103. Deep learning
Byline AffiliationsSchool of Sciences
NAVER LABS, United Kingdom
Institution of OriginUniversity of Southern Queensland
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