AlignSTS: Speech-to-Singing Conversion via Cross-Modal Alignment

• URL: http://arxiv.org/abs/2305.04476v4
• Date: Wed, 24 May 2023 16:37:42 GMT
• Title: AlignSTS: Speech-to-Singing Conversion via Cross-Modal Alignment
• Authors: Ruiqi Li, Rongjie Huang, Lichao Zhang, Jinglin Liu, Zhou Zhao
• Abstract summary: The speech-to-singing (STS) voice conversion task aims to generate singing samples corresponding to speech recordings. This paper proposes AlignSTS, an STS model based on explicit cross-modal alignment. Experiments show that AlignSTS achieves superior performance in terms of both objective and subjective metrics.
• Score: 67.10208647482109
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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