32 citations · 118 across the 11 of their papers we have counts for
11 papers
How to Estimate Model Transferability of Pre-Trained Speech Models?
Zih-Ching Chen, Chao-Han Huck Yang, Bo Li +6
In this work, we introduce a "score-based assessment" framework for estimating the transferability of pre-trained speech models (PSMs) for fine-tuning target tasks. We leverage upo…
A Multi-dimensional Deep Structured State Space Approach to Speech Enhancement Using Small-footprint Models
Pin-Jui Ku, Chao-Han Huck Yang, Sabato Marco Siniscalchi +1
We propose a multi-dimensional structured state space (S4) approach to speech enhancement. To better capture the spectral dependencies across the frequency axis, we focus on modify…
A Neural State-Space Model Approach to Efficient Speech Separation
Chen Chen, Chao-Han Huck Yang, Kai Li +3
In this work, we introduce S4M, a new efficient speech separation framework based on neural state-space models (SSM). Motivated by linear time-invariant systems for sequence modeli…
Differentially Private Adapters for Parameter Efficient Acoustic Modeling
Chun-Wei Ho, Chao-Han Huck Yang, Sabato Marco Siniscalchi
In this work, we devise a parameter-efficient solution to bring differential privacy (DP) guarantees into adaptation of a cross-lingual speech classifier. We investigate a new froz…
Parameter-Efficient Learning for Text-to-Speech Accent Adaptation
Li-Jen Yang, Chao-Han Huck Yang, Jen-Tzung Chien
This paper presents a parameter-efficient learning (PEL) to develop a low-resource accent adaptation for text-to-speech (TTS). A resource-efficient adaptation from a frozen pre-tra…
A Parameter-Efficient Learning Approach to Arabic Dialect Identification with Pre-Trained General-Purpose Speech Model
Srijith Radhakrishnan, Chao-Han Huck Yang, Sumeer Ahmad Khan +3
In this work, we explore Parameter-Efficient-Learning (PEL) techniques to repurpose a General-Purpose-Speech (GSM) model for Arabic dialect identification (ADI). Specifically, we i…
An Experimental Study on Private Aggregation of Teacher Ensemble Learning for End-to-End Speech Recognition
Chao-Han Huck Yang, I-Fan Chen, Andreas Stolcke +2
Differential privacy (DP) is one data protection avenue to safeguard user information used for training deep models by imposing noisy distortion on privacy data. Such a noise pertu…
WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit
Binbin Zhang, Di Wu, Zhendong Peng +7
Recently, we made available WeNet, a production-oriented end-to-end speech recognition toolkit, which introduces a unified two-pass (U2) framework and a built-in runtime to address…
WenetSpeech: A 10000+ Hours Multi-domain Mandarin Corpus for Speech Recognition
Binbin Zhang, Hang Lv, Pengcheng Guo +9
In this paper, we present WenetSpeech, a multi-domain Mandarin corpus consisting of 10000+ hours high-quality labeled speech, 2400+ hours weakly labeled speech, and about 10000 hou…
U2++: Unified Two-pass Bidirectional End-to-end Model for Speech Recognition
Di Wu, Binbin Zhang, Chao Yang +4
The unified streaming and non-streaming two-pass (U2) end-to-end model for speech recognition has shown great performance in terms of streaming capability, accuracy, real-time fact…
WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit
Zhuoyuan Yao, Di Wu, Xiong Wang +7
In this paper, we propose an open source, production first, and production ready speech recognition toolkit called WeNet in which a new two-pass approach is implemented to unify st…