Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/apsipaasc65261.2025.11249197

Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/apsipaasc65261.2025.11249347

Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/apsipaasc65261.2025.11249163

Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/apsipaasc65261.2025.11248977

Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/apsipaasc65261.2025.11249106

Language：English   Publishing type：Research paper (international conference proceedings)  

Publishing type：Research paper (scientific journal)  

DOI： 10.1109/JSTSP.2025.3584195

Web of Science

Scopus

Publisher：ICASSP IEEE International Conference on Acoustics Speech and Signal Processing Proceedings  

Singing voice synthesis (SVS) technology has progressed rapidly in recent years. However, vocal ensemble synthesis has not yet been widely explored. In this work, we focus on unison singing, which is to have several singers singing the same melody together. Our goal is to understand what acoustic properties affect the naturalness of the synthesized unison singing.We utilize NNSVS, an SVS toolkit that allows us to manipulate individual acoustic features, including timing, f0, and spectrum features, in a fully data-driven manner to investigate their effect in unison singing synthesis. Through listening tests, it was shown that the fluctuation in timing and f0 is an important factor in synthesizing natural unison singing. Furthermore, we discovered the potential to generate unison singing using an SVS model trained only with a single singer dataset.

DOI： 10.1109/ICASSP49660.2025.10889744

Scopus

Publisher：European Signal Processing Conference  

We propose Serenade, a novel framework for the singing style conversion (SSC) task. Although singer identity conversion has made great strides in the previous years, converting the singing style of a singer has been an unexplored research area. We find three main challenges in SSC: modeling the target style, disentangling source style, and retaining the source melody. To model the target singing style, we use an audio infilling task by predicting a masked segment of the target mel-spectrogram with a flow-matching model using the complement of the masked target mel-spectrogram along with disentangled acoustic features. On the other hand, to disentangle the source singing style, we use a cyclic training approach, where we use synthetic converted samples as source inputs and reconstruct the original source mel-spectrogram as a target. Finally, to retain the source melody better, we investigate a post-processing module using a source-filter-based vocoder and resynthesize the converted waveforms using the original F0 patterns. Our results showed that the Serenade framework can handle generalized SSC tasks with the best overall similarity score, especially in modeling breathy and mixed singing styles. We also found that resynthesizing with the original F0 patterns alleviated out-of-tune singing and improved naturalness, but found a slight tradeoff in similarity due to not changing the F0 patterns into the target style.

DOI： 10.23919/EUSIPCO63237.2025.11226227

Scopus

Publishing type：Research paper (international conference proceedings)  

Other Link： https://dblp.uni-trier.de/rec/conf/eusipco/2025

Publisher：European Signal Processing Conference  

Source-filter HiFi-GAN (SiFi-GAN) is a neural vocoder offering fast, high-quality voice synthesis with fundamental frequency (F0) controllability. However, SiFi-GAN takes hand-crafted acoustic features from traditional signal processing as input, causing some limitations, such as sound quality degradation in F0 extrapolation. This paper proposes VAE-SiFiGAN, which learns latent representations from Mel-spectrograms via a variational autoencoder (VAE). The latent representations learned through the probabilistic framework enable SiFi-GAN to better model the stochastic components in speech signals, achieving sound quality improvements in F0 modification. Furthermore, to address the insufficient F0 controllability caused by the entanglement of Mel-spectrograms and F0 information, we propose to guide the latent representation learning process with hand-crafted features less affected by F0 and used only during training. Experimental results show that VAE-SiFiGAN achieves superior F0 controllability compared to SiFi-GAN.

DOI： 10.23919/EUSIPCO63237.2025.11226579

Scopus

Publishing type：Research paper (international conference proceedings)  

Other Link： https://dblp.uni-trier.de/rec/conf/eusipco/2025

Language：English   Publishing type：Research paper (scientific journal)   Publisher：Emerald  

DOI： 10.1561/116.20250016

Open Access

Web of Science

Scopus

Web of Science

Language：English   Publishing type：Research paper (scientific journal)   Publisher：ACOUSTICAL SOCIETY OF JAPAN  

<p>Evaluating synthetic speech generated by machines is a complicated process, as it involves judging along multiple dimensions including naturalness, intelligibility, and whether the intended purpose is fulfilled. While subjective listening tests conducted with human participants have been the gold standard for synthetic speech evaluation, its costly process design has also motivated the development of automated objective evaluation protocols. In this review, we first provide a historical view of listening test methodologies, from early in-lab comprehension tests to recent large-scale crowdsourcing mean opinion score (MOS) tests. We then recap the development of automatic measures, ranging from signal-based metrics to model-based approaches that utilize deep neural networks or even the latest self-supervised learning techniques. We also describe the VoiceMOS Challenge series, a scientific event we founded that aims to promote the development of data-driven synthetic speech evaluation. Finally, we provide insights into unsolved issues in this field as well as future prospects. This review is expected to serve as an entry point for early academic researchers to enrich their knowledge in this field, as well as speech synthesis practitioners to catch up on the latest developments.</p>

DOI： 10.1250/ast.e24.12

Web of Science

Scopus

CiNii Research

Language：Japanese   Publisher：Acoustical Society of Japan  

DOI： 10.20697/jasj.80.7_381

CiNii Research

Publishing type：Research paper (scientific journal)  

DOI： 10.1109/TASLP.2024.3402557

Web of Science

Scopus

Publishing type：Research paper (scientific journal)  

DOI： 10.1109/TASLP.2024.3389631

Web of Science

Scopus

Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/ICASSP48485.2024.10447197

Web of Science

Scopus

Other Link： https://dblp.uni-trier.de/db/conf/icassp/icassp2024.html#VioletaHMYKT24

J-GLOBAL

Publisher：Proceedings of 2024 IEEE Spoken Language Technology Workshop Slt 2024  

We present the third edition of the VoiceMOS Challenge, a scientific initiative designed to advance research into automatic prediction of human speech ratings. There were three tracks. The first track was on predicting the quality of 'zoomed-in' high-quality samples from speech synthesis systems. The second track was to predict ratings of samples from singing voice synthesis and voice conversion with a large variety of systems, listeners, and languages. The third track was semi-supervised quality prediction for noisy, clean, and enhanced speech, where a very small amount of labeled training data was provided. Among the eight teams from both academia and industry, we found that many were able to outperform the baseline systems. Successful techniques included retrieval-based methods and the use of non-self-supervised representations like spectrograms and pitch histograms. These results showed that the challenge has advanced the field of subjective speech rating prediction.

DOI： 10.1109/SLT61566.2024.10832295

Web of Science

Scopus

Publishing type：Research paper (scientific journal)  

DOI： 10.1109/LSP.2024.3482701

Web of Science

Scopus