4 citations · 8 across the 13 of their papers we have counts for
14 papers
Advancing Electrolaryngeal Speech Enhancement Through Speech-Text Representation Learning
Ding Ma, Jinyi Mi, Fengji Li +5
Objective: laryngectomees depend on an electromechanical device to generate electrolaryngeal (EL) speech. Compared with normal speech, EL speech suffers from severe distortion, lim…
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…
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…
A Preliminary Investigation on Flexible Singing Voice Synthesis Through Decomposed Framework with Inferrable Features
Lester Phillip Violeta, Taketo Akama
We investigate the feasibility of a singing voice synthesis (SVS) system by using a decomposed framework to improve flexibility in generating singing voices. Due to data-driven app…
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…
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)…
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…
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…
An Analysis of Personalized Speech Recognition System Development for the Deaf and Hard-of-Hearing
Lester Phillip Violeta, Tomoki Toda
Deaf or hard-of-hearing (DHH) speakers typically have atypical speech caused by deafness. With the growing support of speech-based devices and software applications, more work need…
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…
Two-stage training method for Japanese electrolaryngeal speech enhancement based on sequence-to-sequence voice conversion
Ding Ma, Lester Phillip Violeta, Kazuhiro Kobayashi +1
Sequence-to-sequence (seq2seq) voice conversion (VC) models have greater potential in converting electrolaryngeal (EL) speech to normal speech (EL2SP) compared to conventional VC m…
Investigating Self-supervised Pretraining Frameworks for Pathological Speech Recognition
Lester Phillip Violeta, Wen-Chin Huang, Tomoki Toda
We investigate the performance of self-supervised pretraining frameworks on pathological speech datasets used for automatic speech recognition (ASR). Modern end-to-end models requi…
Towards Identity Preserving Normal to Dysarthric Voice Conversion
Wen-Chin Huang, Bence Mark Halpern, Lester Phillip Violeta +2
We present a voice conversion framework that converts normal speech into dysarthric speech while preserving the speaker identity. Such a framework is essential for (1) clinical dec…