262 citations · 545 across the 57 of their papers we have counts for
60 papers
NeuMark-Native: Robust Text-to-Speech-Native Watermarking Through Full Utilization of Neural Audio Codec Latent Space
Annan Wu, Wen-Chin Huang, Tomoki Toda
Speech watermarking offers proactive traceability for synthetic speech, yet most existing models operate only after text-to-speech (TTS) synthesis by adding a watermark perturbatio…
NeuMark: Neural Codec Resynthesis-Robust Audio Watermarking in the Codec Latent Space
Annan Wu, Wen-Chin Huang, Tomoki Toda
Audio watermarking is increasingly important for tracing generated speech. Several audio watermarking methods have been proposed to embed the watermark in various domains, such as…
Attacking UTMOS: Probing the Robustness of a Speech Quality Assessment Model
Wen-Chin Huang, Tomoki Toda
UTMOS has become one of the most commonly used deep neural network-based speech quality assessment (SQA) metrics in speech processing research. In this paper, we attack UTMOS to pr…
RAS: a Reliability Oriented Metric for Automatic Speech Recognition
Wenbin Huang, Yuhang Qiu, Bohan Li +5
Automatic speech recognition systems often produce confident yet incorrect transcriptions under noisy or ambiguous conditions, which can be misleading for both users and downstream…
MOS-Bias: From Hidden Gender Bias to Gender-Aware Speech Quality Assessment
Wenze Ren, Yi-Cheng Lin, Wen-Chin Huang +5
The Mean Opinion Score (MOS) serves as the standard metric for speech quality assessment, yet biases in human annotations remain underexplored. We conduct the first systematic anal…
An Extensive Analysis of the Singing Voice Conversion Challenge 2025 Evaluation Results
Lester Phillip Violeta, Xueyao Zhang, Jiatong Shi +4
We present a thorough analysis of the findings of the latest iteration of the Singing Voice Conversion Challenge, a scientific event aiming to compare and understand different voic…
The AudioMOS Challenge 2025
Wen-Chin Huang, Hui Wang, Cheng Liu +6
This is the summary paper for the AudioMOS Challenge 2025, the very first challenge for automatic subjective quality prediction for synthetic audio. The challenge consists of three…
Advancing Speech Quality Assessment Through Scientific Challenges and Open-source Activities
Wen-Chin Huang
Speech quality assessment (SQA) refers to the evaluation of speech quality, and developing an accurate automatic SQA method that reflects human perception has become increasingly i…
SHEET: A Multi-purpose Open-source Speech Human Evaluation Estimation Toolkit
Wen-Chin Huang, Erica Cooper, Tomoki Toda
We introduce SHEET, a multi-purpose open-source toolkit designed to accelerate subjective speech quality assessment (SSQA) research. SHEET stands for the Speech Human Evaluation Es…
Music Similarity Representation Learning Focusing on Individual Instruments with Source Separation and Human Preference
Takehiro Imamura, Yuka Hashizume, Wen-Chin Huang +1
This paper proposes music similarity representation learning (MSRL) based on individual instrument sounds (InMSRL) utilizing music source separation (MSS) and human preference with…
Serenade: A Singing Style Conversion Framework Based On Audio Infilling
Lester Phillip Violeta, Wen-Chin Huang, Tomoki Toda
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…
MOS-Bench: Benchmarking Generalization Abilities of Subjective Speech Quality Assessment Models
Wen-Chin Huang, Erica Cooper, Tomoki Toda
In this paper, we study the task of subjective speech quality assessment (SSQA), which refers to predicting the perceptual quality of speech. Owing to the development of deep neura…
The VoiceMOS Challenge 2024: Beyond Speech Quality Prediction
Wen-Chin Huang, Szu-Wei Fu, Erica Cooper +5
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 tra…
Quantifying the effect of speech pathology on automatic and human speaker verification
Bence Mark Halpern, Thomas Tienkamp, Wen-Chin Huang +7
This study investigates how surgical intervention for speech pathology (specifically, as a result of oral cancer surgery) impacts the performance of an automatic speaker verificati…
Multi-speaker Text-to-speech Training with Speaker Anonymized Data
Wen-Chin Huang, Yi-Chiao Wu, Tomoki Toda
The trend of scaling up speech generation models poses a threat of biometric information leakage of the identities of the voices in the training data, raising privacy and security…
A Comparative Study of Voice Conversion Models with Large-Scale Speech and Singing Data: The T13 Systems for the Singing Voice Conversion Challenge 2023
Ryuichi Yamamoto, Reo Yoneyama, Lester Phillip Violeta +2
This paper presents our systems (denoted as T13) for the singing voice conversion challenge (SVCC) 2023. For both in-domain and cross-domain English singing voice conversion (SVC)…
The VoiceMOS Challenge 2023: Zero-shot Subjective Speech Quality Prediction for Multiple Domains
Erica Cooper, Wen-Chin Huang, Yu Tsao +3
We present the second edition of the VoiceMOS Challenge, a scientific event that aims to promote the study of automatic prediction of the mean opinion score (MOS) of synthesized an…
Improving severity preservation of healthy-to-pathological voice conversion with global style tokens
Bence Mark Halpern, Wen-Chin Huang, Lester Phillip Violeta +2
In healthy-to-pathological voice conversion (H2P-VC), healthy speech is converted into pathological while preserving the identity. The paper improves on previous two-stage approach…
AAS-VC: On the Generalization Ability of Automatic Alignment Search based Non-autoregressive Sequence-to-sequence Voice Conversion
Wen-Chin Huang, Kazuhiro Kobayashi, Tomoki Toda
Non-autoregressive (non-AR) sequence-to-seqeunce (seq2seq) models for voice conversion (VC) is attractive in its ability to effectively model the temporal structure while enjoying…
Evaluating Methods for Ground-Truth-Free Foreign Accent Conversion
Wen-Chin Huang, Tomoki Toda
Foreign accent conversion (FAC) is a special application of voice conversion (VC) which aims to convert the accented speech of a non-native speaker to a native-sounding speech with…
Electrolaryngeal Speech Intelligibility Enhancement Through Robust Linguistic Encoders
Lester Phillip Violeta, Wen-Chin Huang, Ding Ma +3
We propose a novel framework for electrolaryngeal speech intelligibility enhancement through the use of robust linguistic encoders. Pretraining and fine-tuning approaches have prov…
The Singing Voice Conversion Challenge 2023
Wen-Chin Huang, Lester Phillip Violeta, Songxiang Liu +2
We present the latest iteration of the voice conversion challenge (VCC) series, a bi-annual scientific event aiming to compare and understand different voice conversion (VC) system…
A Holistic Cascade System, benchmark, and Human Evaluation Protocol for Expressive Speech-to-Speech Translation
Wen-Chin Huang, Benjamin Peloquin, Justine Kao +6
Expressive speech-to-speech translation (S2ST) aims to transfer prosodic attributes of source speech to target speech while maintaining translation accuracy. Existing research in e…
Intermediate Fine-Tuning Using Imperfect Synthetic Speech for Improving Electrolaryngeal Speech Recognition
Lester Phillip Violeta, Ding Ma, Wen-Chin Huang +1
Research on automatic speech recognition (ASR) systems for electrolaryngeal speakers has been relatively unexplored due to small datasets. When training data is lacking in ASR, a l…