262 citations · 1.4k across the 109 of their papers we have counts for
26 papers · 1 filter
BASPRO: a balanced script producer for speech corpus collection based on the genetic algorithm
Yu-Wen Chen, Hsin-Min Wang, Yu Tsao
The performance of speech-processing models is heavily influenced by the speech corpus that is used for training and evaluation. In this study, we propose BAlanced Script PROducer…
T5lephone: Bridging Speech and Text Self-supervised Models for Spoken Language Understanding via Phoneme level T5
Chan-Jan Hsu, Ho-Lam Chung, Hung-yi Lee +1
In Spoken language understanding (SLU), a natural solution is concatenating pre-trained speech models (e.g. HuBERT) and pretrained language models (PLM, e.g. T5). Most previous wor…
CasNet: Investigating Channel Robustness for Speech Separation
Fan-Lin Wang, Yao-Fei Cheng, Hung-Shin Lee +2
Recording channel mismatch between training and testing conditions has been shown to be a serious problem for speech separation. This situation greatly reduces the separation perfo…
A Training and Inference Strategy Using Noisy and Enhanced Speech as Target for Speech Enhancement without Clean Speech
Li-Wei Chen, Yao-Fei Cheng, Hung-Shin Lee +2
The lack of clean speech is a practical challenge to the development of speech enhancement systems, which means that there is an inevitable mismatch between their training criterio…
Mandarin Singing Voice Synthesis with Denoising Diffusion Probabilistic Wasserstein GAN
Yin-Ping Cho, Yu Tsao, Hsin-Min Wang +1
Singing voice synthesis (SVS) is the computer production of a human-like singing voice from given musical scores. To accomplish end-to-end SVS effectively and efficiently, this wor…
ESPnet-SE++: Speech Enhancement for Robust Speech Recognition, Translation, and Understanding
Yen-Ju Lu, Xuankai Chang, Chenda Li +10
This paper presents recent progress on integrating speech separation and enhancement (SSE) into the ESPnet toolkit. Compared with the previous ESPnet-SE work, numerous features hav…
NASTAR: Noise Adaptive Speech Enhancement with Target-Conditional Resampling
Chi-Chang Lee, Cheng-Hung Hu, Yu-Chen Lin +3
For deep learning-based speech enhancement (SE) systems, the training-test acoustic mismatch can cause notable performance degradation. To address the mismatch issue, numerous nois…
EPG2S: Speech Generation and Speech Enhancement based on Electropalatography and Audio Signals using Multimodal Learning
Li-Chin Chen, Po-Hsun Chen, Richard Tzong-Han Tsai +1
Speech generation and enhancement based on articulatory movements facilitate communication when the scope of verbal communication is absent, e.g., in patients who have lost the abi…
Filter-based Discriminative Autoencoders for Children Speech Recognition
Chiang-Lin Tai, Hung-Shin Lee, Yu Tsao +1
Children speech recognition is indispensable but challenging due to the diversity of children's speech. In this paper, we propose a filter-based discriminative autoencoder for acou…
XDBERT: Distilling Visual Information to BERT from Cross-Modal Systems to Improve Language Understanding
Chan-Jan Hsu, Hung-yi Lee, Yu Tsao
Transformer-based models are widely used in natural language understanding (NLU) tasks, and multimodal transformers have been effective in visual-language tasks. This study explore…
A Study of Using Cepstrogram for Countermeasure Against Replay Attacks
Shih-Kuang Lee, Yu Tsao, Hsin-Min Wang
This study investigated the cepstrogram properties and demonstrated their effectiveness as powerful countermeasures against replay attacks. A cepstrum analysis of replay attacks su…
MTI-Net: A Multi-Target Speech Intelligibility Prediction Model
Ryandhimas E. Zezario, Szu-wei Fu, Fei Chen +3
Recently, deep learning (DL)-based non-intrusive speech assessment models have attracted great attention. Many studies report that these DL-based models yield satisfactory assessme…
MBI-Net: A Non-Intrusive Multi-Branched Speech Intelligibility Prediction Model for Hearing Aids
Ryandhimas E. Zezario, Fei Chen, Chiou-Shann Fuh +2
Improving the user's hearing ability to understand speech in noisy environments is critical to the development of hearing aid (HA) devices. For this, it is important to derive a me…
Partial Coupling of Optimal Transport for Spoken Language Identification
Xugang Lu, Peng Shen, Yu Tsao +1
In order to reduce domain discrepancy to improve the performance of cross-domain spoken language identification (SLID) system, as an unsupervised domain adaptation (UDA) method, we…
Subspace-based Representation and Learning for Phonotactic Spoken Language Recognition
Hung-Shin Lee, Yu Tsao, Shyh-Kang Jeng +1
Phonotactic constraints can be employed to distinguish languages by representing a speech utterance as a multinomial distribution or phone events. In the present study, we propose…
Perceptual Contrast Stretching on Target Feature for Speech Enhancement
Rong Chao, Cheng Yu, Szu-Wei Fu +2
Speech enhancement (SE) performance has improved considerably owing to the use of deep learning models as a base function. Herein, we propose a perceptual contrast stretching (PCS)…
Disentangling the Impacts of Language and Channel Variability on Speech Separation Networks
Fan-Lin Wang, Hung-Shin Lee, Yu Tsao +1
Because the performance of speech separation is excellent for speech in which two speakers completely overlap, research attention has been shifted to dealing with more realistic sc…
Multi-target Extractor and Detector for Unknown-number Speaker Diarization
Chin-Yi Cheng, Hung-Shin Lee, Yu Tsao +1
Strong representations of target speakers can help extract important information about speakers and detect corresponding temporal regions in multi-speaker conversations. In this st…
Speech-enhanced and Noise-aware Networks for Robust Speech Recognition
Hung-Shin Lee, Pin-Yuan Chen, Yao-Fei Cheng +2
Compensation for channel mismatch and noise interference is essential for robust automatic speech recognition. Enhanced speech has been introduced into the multi-condition training…
The VoiceMOS Challenge 2022
Wen-Chin Huang, Erica Cooper, Yu Tsao +3
We present the first edition of the VoiceMOS Challenge, a scientific event that aims to promote the study of automatic prediction of the mean opinion score (MOS) of synthetic speec…
Continuous Speech for Improved Learning Pathological Voice Disorders
Syu-Siang Wang, Chi-Te Wang, Chih-Chung Lai +2
Goal: Numerous studies had successfully differentiated normal and abnormal voice samples. Nevertheless, further classification had rarely been attempted. This study proposes a nove…
A Speech Intelligibility Enhancement Model based on Canonical Correlation and Deep Learning for Hearing-Assistive Technologies
Tassadaq Hussain, Muhammad Diyan, Mandar Gogate +4
Current deep learning (DL) based approaches to speech intelligibility enhancement in noisy environments are generally trained to minimise the distance between clean and enhanced sp…
EMGSE: Acoustic/EMG Fusion for Multimodal Speech Enhancement
Kuan-Chen Wang, Kai-Chun Liu, Hsin-Min Wang +1
Multimodal learning has been proven to be an effective method to improve speech enhancement (SE) performance, especially in challenging situations such as low signal-to-noise ratio…
A Novel Speech Intelligibility Enhancement Model based on CanonicalCorrelation and Deep Learning
Tassadaq Hussain, Muhammad Diyan, Mandar Gogate +4
Current deep learning (DL) based approaches to speech intelligibility enhancement in noisy environments are often trained to minimise the feature distance between noise-free speech…