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NHSS: A Speech and Singing Parallel Database

arXiv 2012.00337 ↗First posted 1 Dec 2020cs.SD cs.HC eess.AScited by 16

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Abstract (arXiv)

We present a database of parallel recordings of speech and singing, collected and released by the Human Language Technology (HLT) laboratory at the National University of Singapore (NUS), that is called NUS-HLT Speak-Sing (NHSS) database. We release this database to the public to support research activities, that include, but not limited to comparative studies of acoustic attributes of speech and singing signals, cooperative synthesis of speech and singing voices, and speech-to-singing conversion. This database consists of recordings of sung vocals of English pop songs, the spoken counterpart of lyrics of the songs read by the singers in their natural reading manner, and manually prepared utterance-level and word-level annotations. The audio recordings in the NHSS database correspond to 100 songs sung and spoken by 10 singers, resulting in a total of 7 hours of audio data. There are 5 male and 5 female singers, singing and reading the lyrics of 10 songs each. In this paper, we discuss the design methodology of the database, analyse the similarities and dissimilarities in characteristics of speech and singing voices, and provide some strategies to address relationships between these characteristics for converting one to another. We develop benchmark systems, which can be used as reference for speech-to-singing alignment, spectral mapping, and conversion using the NHSS database.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Dec 2020.

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Citing papers per year: 2021: 1; 2022: 4; 2023: 4; 2024: 4; 2025: 2; 2026: 1.

16 papers.

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  1. 2023GibbsDDRM: A Partially Collapsed Gibbs Sampler for Solving Blind Inverse Problems with Denoising Diffusion Restorationcited by 58
  2. 2024GTSinger: A Global Multi-Technique Singing Corpus with Realistic Music Scores for All Singing Taskscited by 29
  3. 2023The Singing Voice Conversion Challenge 2023cited by 25
  4. 2022Unsupervised vocal dereverberation with diffusion-based generative modelscited by 18
  5. 2024Unsupervised Blind Joint Dereverberation and Room Acoustics Estimation with Diffusion Modelscited by 11
  6. 2022Hierarchical Diffusion Models for Singing Voice Neural Vocodercited by 10
  7. 2024SongComposer: A Large Language Model for Lyric and Melody Generation in Song Compositioncited by 9
  8. 2024RapVerse: Coherent Vocals and Whole-Body Motions Generations from Textcited by 7
  9. 2022Robust One-Shot Singing Voice Conversioncited by 5
  10. 2022Leveraging Symmetrical Convolutional Transformer Networks for Speech to Singing Voice Style Transfercited by 2
  11. 2023A Comparative Study of Voice Conversion Models with Large-Scale Speech and Singing Data: The T13 Systems for the Singing Voice Conversion Challenge 2023cited by 2
  12. 2025HQ-SVC: Towards High-Quality Zero-Shot Singing Voice Conversion in Low-Resource Scenarioscited by 2
4 more
  1. 2021DeepA: A Deep Neural Analyzer For Speech And Singing Vocodingcited by 1
  2. 2023Iteratively Improving Speech Recognition and Voice Conversioncited by 1
  3. 2025Perceptually Aligning Representations of Music via Noise-Augmented Autoencoderscited by 0
  4. 2026Music Restoration via Latent Operator Optimization and Diffusion Model Priorscited by 0