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Related Experiment Video

Updated: May 22, 2025

Author Spotlight: Advancements in the Fabrication of Synthetic Vocal Fold Models for Phonetic and Robotic Applications
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Singing to speech conversion with generative flow.

Jiawen Huang1, Emmanouil Benetos1

  • 1Centre for Digital Music, Queen Mary University of London, London, UK.

EURASIP Journal on Audio, Speech, and Music Processing
|March 13, 2025
PubMed
Summary
This summary is machine-generated.

This study presents the first deep learning system for singing to speech (S2S) conversion, transforming singing into natural-sounding speech while preserving phonetic details. The S2S model shows promise for improving low-resource lyrics transcription.

Keywords:
Duration manipulationGenerative flowLyrics transcriptionPhonetic similaritySinging voice conversion

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Area of Science:

  • Artificial Intelligence
  • Speech Processing
  • Machine Learning

Background:

  • Singing to speech (S2S) conversion is a novel cross-domain task.
  • Existing methods lack naturalness and phonetic accuracy.
  • Deep learning approaches offer potential for S2S advancement.

Purpose of the Study:

  • Introduce the first deep learning-based S2S system.
  • Transform singing into speech, preserving phonetic content.
  • Enhance naturalness and phonetic similarity in converted speech.

Main Methods:

  • Developed a generative flow model inspired by Glow-TTS.
  • Adapted monotonic alignment search (MAS) for S2S.
  • Utilized a duration predictor to handle modality differences.

Main Results:

  • The proposed deep learning model outperforms signal processing baselines in naturalness.
  • It surpasses transcribe-and-synthesize methods in phonetic similarity.
  • S2S conversion proves effective for low-resource lyrics transcription.

Conclusions:

  • The novel deep learning S2S system achieves high naturalness and phonetic accuracy.
  • This technology offers a viable solution for voice conversion challenges.
  • S2S conversion can serve as a valuable data augmentation technique.