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A comparative study of self-supervised speech representationsin read and spontaneous TTS
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Speech, Music and Hearing, TMH.
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Speech, Music and Hearing, TMH.ORCID iD: 0000-0002-1643-1054
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Speech, Music and Hearing, TMH.ORCID iD: 0000-0002-0397-6442
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Speech, Music and Hearing, TMH.ORCID iD: 0000-0003-1175-840X
2023 (English)Manuscript (preprint) (Other academic)
Abstract [en]

Recent work has explored using self-supervised learning(SSL) speech representations such as wav2vec2.0 as the rep-resentation medium in standard two-stage TTS, in place ofconventionally used mel-spectrograms. It is however unclearwhich speech SSL is the better fit for TTS, and whether ornot the performance differs between read and spontaneousTTS, the later of which is arguably more challenging. Thisstudy aims at addressing these questions by testing severalspeech SSLs, including different layers of the same SSL, intwo-stage TTS on both read and spontaneous corpora, whilemaintaining constant TTS model architecture and trainingsettings. Results from listening tests show that the 9th layerof 12-layer wav2vec2.0 (ASR finetuned) outperforms othertested SSLs and mel-spectrogram, in both read and sponta-neous TTS. Our work sheds light on both how speech SSL canreadily improve current TTS systems, and how SSLs comparein the challenging generative task of TTS. Audio examplescan be found at https://www.speech.kth.se/tts-demos/ssr tts

Place, publisher, year, edition, pages
2023.
Keywords [en]
speech synthesis, self-supervised speech representation, spontaneous speech
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering Interaction Technologies
Research subject
Speech and Music Communication
Identifiers
URN: urn:nbn:se:kth:diva-328741ISBN: 979-8-3503-0261-5 (print)OAI: oai:DiVA.org:kth-328741DiVA, id: diva2:1765841
Conference
2023 IEEE International Conference on Acoustics, Speech,and Signal Processing Workshops, 4-10 Jun 2023, Rhodes Island, Greece
Projects
Digital Futures project Advanced Adaptive Intelligent Systems (AAIS)Swedish Research Council project Connected (VR-2019-05003)Swedish Research Council project Perception of speaker stance (VR-2020- 02396)Riksbankens Jubileumsfond project CAPTivating (P20-0298)Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation
Note

Accepted by the 2023 IEEE International Conference on Acoustics, Speech,and Signal Processing Workshops, 4-10 Jun 2023, Rhodes Island, Greece

QC 20230620

Available from: 2023-06-12 Created: 2023-06-12 Last updated: 2023-06-20Bibliographically approved

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Authority records

Wang, SiyangHenter, Gustav EjeGustafsson, JoakimSzékely, Éva

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CiteExportLink to record
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Citation style
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Output format
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