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    <abstract>The speech-to-singing (STS) voice conversion task aims to generate singing samples corresponding to speech recordings while facing a major challenge: the alignment between the target (singing) pitch contour and the source (speech) content is difficult to learn in a text-free situation. This paper proposes AlignSTS, an STS model based on explicit cross-modal alignment, which views speech variance such as pitch and content as different modalities. Inspired by the mechanism of how humans will sing the lyrics to the melody, AlignSTS: 1) adopts a novel rhythm adaptor to predict the target rhythm representation to bridge the modality gap between content and pitch, where the rhythm representation is computed in a simple yet effective way and is quantized into a discrete space; and 2) uses the predicted rhythm representation to re-align the content based on cross-attention and conducts a cross-modal fusion for re-synthesize. Extensive experiments show that AlignSTS achieves superior performance in terms of both objective and subjective metrics. Audio samples are available at https://alignsts.github.io.</abstract>
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%0 Conference Proceedings
%T AlignSTS: Speech-to-Singing Conversion via Cross-Modal Alignment
%A Li, Ruiqi
%A Huang, Rongjie
%A Zhang, Lichao
%A Liu, Jinglin
%A Zhao, Zhou
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F li-etal-2023-alignsts
%X The speech-to-singing (STS) voice conversion task aims to generate singing samples corresponding to speech recordings while facing a major challenge: the alignment between the target (singing) pitch contour and the source (speech) content is difficult to learn in a text-free situation. This paper proposes AlignSTS, an STS model based on explicit cross-modal alignment, which views speech variance such as pitch and content as different modalities. Inspired by the mechanism of how humans will sing the lyrics to the melody, AlignSTS: 1) adopts a novel rhythm adaptor to predict the target rhythm representation to bridge the modality gap between content and pitch, where the rhythm representation is computed in a simple yet effective way and is quantized into a discrete space; and 2) uses the predicted rhythm representation to re-align the content based on cross-attention and conducts a cross-modal fusion for re-synthesize. Extensive experiments show that AlignSTS achieves superior performance in terms of both objective and subjective metrics. Audio samples are available at https://alignsts.github.io.
%R 10.18653/v1/2023.findings-acl.442
%U https://aclanthology.org/2023.findings-acl.442
%U https://doi.org/10.18653/v1/2023.findings-acl.442
%P 7074-7088

Markdown (Informal)

[AlignSTS: Speech-to-Singing Conversion via Cross-Modal Alignment](https://aclanthology.org/2023.findings-acl.442) (Li et al., Findings 2023)

• AlignSTS: Speech-to-Singing Conversion via Cross-Modal Alignment (Li et al., Findings 2023)

ACL

• Ruiqi Li, Rongjie Huang, Lichao Zhang, Jinglin Liu, and Zhou Zhao. 2023. AlignSTS: Speech-to-Singing Conversion via Cross-Modal Alignment. In Findings of the Association for Computational Linguistics: ACL 2023, pages 7074–7088, Toronto, Canada. Association for Computational Linguistics.