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Cross-utterance Conditioned Coherent Speech Editing

Yu, C; Li, Y; Zu, W; Sun, F; Tian, Z; Wang, J; (2023) Cross-utterance Conditioned Coherent Speech Editing. In: Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH. (pp. pp. 2108-2112). ISCA: Dublin, Ireland. Green open access

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Abstract

Text-based speech editing systems are developed to enable users to modify speech based on the transcript. Existing state-of-the-art editing systems based on neural networks do partial inferences with no exception, that is, only generate new words that need to be replaced or inserted. This manner usually leads to the prosody of the edited part being inconsistent with the surrounding speech and a failure to handle the alteration of intonation. To address these problems, we propose a cross-utterance conditioned coherent speech editing system, that first does the entire reasoning at the inference time. Our proposed system can generate speech by utilizing speaker information, context, acoustic features, and the mel-spectrogram from the original audio. Experiments conducted on subjective and objective metrics demonstrate that our approach outperforms the baseline on various editing operations regarding naturalness and prosody consistency.

Type: Proceedings paper
Title: Cross-utterance Conditioned Coherent Speech Editing
Event: INTERSPEECH 2023
Open access status: An open access version is available from UCL Discovery
DOI: 10.21437/Interspeech.2023-2558
Publisher version: http://dx.doi.org/10.21437/Interspeech.2023-2558
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: speech editing, variational autoencoder
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery-pp.ucl.ac.uk/id/eprint/10178175
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