54 citations · 92 across the 16 of their papers we have counts for
18 papers
MoVE: Translating Laughter and Tears via Mixture of Vocalization Experts in Speech-to-Speech Translation
Szu-Chi Chen, I-Ning Tsai, Yi-Cheng Lin +2
Recent Speech-to-Speech Translation (S2ST) systems achieve strong semantic accuracy yet consistently strip away non-verbal vocalizations (NVs), such as laughter and crying that con…
Joint Fullband-Subband Modeling for High-Resolution SingFake Detection
Xuanjun Chen, Chia-Yu Hu, Sung-Feng Huang +3
Rapid advances in singing voice synthesis have increased unauthorized imitation risks, creating an urgent need for better Singing Voice Deepfake (SingFake) Detection, also known as…
VIBE: Voice-Induced open-ended Bias Evaluation for Large Audio-Language Models via Real-World Speech
Yi-Cheng Lin, Yusuke Hirota, Sung-Feng Huang +1
Large Audio-Language Models (LALMs) are increasingly integrated into daily applications, yet their generative biases remain underexplored. Existing speech fairness benchmarks rely…
How Does Instrumental Music Help SingFake Detection?
Xuanjun Chen, Chia-Yu Hu, I-Ming Lin +8
Although many models exist to detect singing voice deepfakes (SingFake), how these models operate, particularly with instrumental accompaniment, is unclear. We investigate how inst…
Maximizing Data Efficiency for Cross-Lingual TTS Adaptation by Self-Supervised Representation Mixing and Embedding Initialization
Wei-Ping Huang, Sung-Feng Huang, Hung-yi Lee
This paper presents an effective transfer learning framework for language adaptation in text-to-speech systems, with a focus on achieving language adaptation using minimal labeled…
Personalized Lightweight Text-to-Speech: Voice Cloning with Adaptive Structured Pruning
Sung-Feng Huang, Chia-ping Chen, Zhi-Sheng Chen +2
Personalized TTS is an exciting and highly desired application that allows users to train their TTS voice using only a few recordings. However, TTS training typically requires many…
Learning Phone Recognition from Unpaired Audio and Phone Sequences Based on Generative Adversarial Network
Da-rong Liu, Po-chun Hsu, Yi-chen Chen +4
ASR has been shown to achieve great performance recently. However, most of them rely on massive paired data, which is not feasible for low-resource languages worldwide. This paper…
Few-Shot Cross-Lingual TTS Using Transferable Phoneme Embedding
Wei-Ping Huang, Po-Chun Chen, Sung-Feng Huang +1
This paper studies a transferable phoneme embedding framework that aims to deal with the cross-lingual text-to-speech (TTS) problem under the few-shot setting. Transfer learning is…
Meta-TTS: Meta-Learning for Few-Shot Speaker Adaptive Text-to-Speech
Sung-Feng Huang, Chyi-Jiunn Lin, Da-Rong Liu +2
Personalizing a speech synthesis system is a highly desired application, where the system can generate speech with the user's voice with rare enrolled recordings. There are two mai…
SpeechNet: A Universal Modularized Model for Speech Processing Tasks
Yi-Chen Chen, Po-Han Chi, Shu-wen Yang +7
There is a wide variety of speech processing tasks ranging from extracting content information from speech signals to generating speech signals. For different tasks, model networks…
Non-autoregressive Mandarin-English Code-switching Speech Recognition
Shun-Po Chuang, Heng-Jui Chang, Sung-Feng Huang +1
Mandarin-English code-switching (CS) is frequently used among East and Southeast Asian people. However, the intra-sentence language switching of the two very different languages ma…
Pretrained Language Model Embryology: The Birth of ALBERT
Cheng-Han Chiang, Sung-Feng Huang, Hung-yi Lee
While behaviors of pretrained language models (LMs) have been thoroughly examined, what happened during pretraining is rarely studied. We thus investigate the developmental process…
Stabilizing Label Assignment for Speech Separation by Self-supervised Pre-training
Sung-Feng Huang, Shun-Po Chuang, Da-Rong Liu +3
Speech separation has been well developed, with the very successful permutation invariant training (PIT) approach, although the frequent label assignment switching happening during…
From Semi-supervised to Almost-unsupervised Speech Recognition with Very-low Resource by Jointly Learning Phonetic Structures from Audio and Text Embeddings
Yi-Chen Chen, Sung-Feng Huang, Hung-yi Lee +1
Producing a large amount of annotated speech data for training ASR systems remains difficult for more than 95% of languages all over the world which are low-resourced. However, we…
Improved Audio Embeddings by Adjacency-Based Clustering with Applications in Spoken Term Detection
Sung-Feng Huang, Yi-Chen Chen, Hung-yi Lee +1
Embedding audio signal segments into vectors with fixed dimensionality is attractive because all following processing will be easier and more efficient, for example modeling, class…
Almost-unsupervised Speech Recognition with Close-to-zero Resource Based on Phonetic Structures Learned from Very Small Unpaired Speech and Text Data
Yi-Chen Chen, Chia-Hao Shen, Sung-Feng Huang +2
Producing a large amount of annotated speech data for training ASR systems remains difficult for more than 95% of languages all over the world which are low-resourced. However, we…
Phonetic-and-Semantic Embedding of Spoken Words with Applications in Spoken Content Retrieval
Yi-Chen Chen, Sung-Feng Huang, Chia-Hao Shen +2
Word embedding or Word2Vec has been successful in offering semantics for text words learned from the context of words. Audio Word2Vec was shown to offer phonetic structures for spo…
Towards Unsupervised Automatic Speech Recognition Trained by Unaligned Speech and Text only
Yi-Chen Chen, Chia-Hao Shen, Sung-Feng Huang +1
Automatic speech recognition (ASR) has been widely researched with supervised approaches, while many low-resourced languages lack audio-text aligned data, and supervised methods ca…