53 citations · 260 across the 63 of their papers we have counts for
13 papers · 1 filter
A Study on the Integration of Pre-trained SSL, ASR, LM and SLU Models for Spoken Language Understanding
Yifan Peng, Siddhant Arora, Yosuke Higuchi +6
Collecting sufficient labeled data for spoken language understanding (SLU) is expensive and time-consuming. Recent studies achieved promising results by using pre-trained models in…
SUPERB @ SLT 2022: Challenge on Generalization and Efficiency of Self-Supervised Speech Representation Learning
Tzu-hsun Feng, Annie Dong, Ching-Feng Yeh +11
We present the SUPERB challenge at SLT 2022, which aims at learning self-supervised speech representation for better performance, generalization, and efficiency. The challenge buil…
End-to-End Integration of Speech Recognition, Dereverberation, Beamforming, and Self-Supervised Learning Representation
Yoshiki Masuyama, Xuankai Chang, Samuele Cornell +2
Self-supervised learning representation (SSLR) has demonstrated its significant effectiveness in automatic speech recognition (ASR), mainly with clean speech. Recent work pointed o…
Two-Pass Low Latency End-to-End Spoken Language Understanding
Siddhant Arora, Siddharth Dalmia, Xuankai Chang +3
End-to-end (E2E) models are becoming increasingly popular for spoken language understanding (SLU) systems and are beginning to achieve competitive performance to pipeline-based app…
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…
Muskits: an End-to-End Music Processing Toolkit for Singing Voice Synthesis
Jiatong Shi, Shuai Guo, Tao Qian +9
This paper introduces a new open-source platform named Muskits for end-to-end music processing, which mainly focuses on end-to-end singing voice synthesis (E2E-SVS). Muskits suppor…
End-to-End Integration of Speech Recognition, Speech Enhancement, and Self-Supervised Learning Representation
Xuankai Chang, Takashi Maekaku, Yuya Fujita +1
This work presents our end-to-end (E2E) automatic speech recognition (ASR) model targetting at robust speech recognition, called Integraded speech Recognition with enhanced speech…
End-to-End Multi-speaker ASR with Independent Vector Analysis
Robin Scheibler, Wangyou Zhang, Xuankai Chang +2
We develop an end-to-end system for multi-channel, multi-speaker automatic speech recognition. We propose a frontend for joint source separation and dereverberation based on the in…
SUPERB-SG: Enhanced Speech processing Universal PERformance Benchmark for Semantic and Generative Capabilities
Hsiang-Sheng Tsai, Heng-Jui Chang, Wen-Chin Huang +14
Transfer learning has proven to be crucial in advancing the state of speech and natural language processing research in recent years. In speech, a model pre-trained by self-supervi…
Extended Graph Temporal Classification for Multi-Speaker End-to-End ASR
Xuankai Chang, Niko Moritz, Takaaki Hori +2
Graph-based temporal classification (GTC), a generalized form of the connectionist temporal classification loss, was recently proposed to improve automatic speech recognition (ASR)…
Towards Low-distortion Multi-channel Speech Enhancement: The ESPNet-SE Submission to The L3DAS22 Challenge
Yen-Ju Lu, Samuele Cornell, Xuankai Chang +5
This paper describes our submission to the L3DAS22 Challenge Task 1, which consists of speech enhancement with 3D Ambisonic microphones. The core of our approach combines Deep Neur…
Joint Speech Recognition and Audio Captioning
Chaitanya Narisetty, Emiru Tsunoo, Xuankai Chang +3
Speech samples recorded in both indoor and outdoor environments are often contaminated with secondary audio sources. Most end-to-end monaural speech recognition systems either remo…
Discretization and Re-synthesis: an alternative method to solve the Cocktail Party Problem
Jing Shi, Xuankai Chang, Tomoki Hayashi +3
Deep learning based models have significantly improved the performance of speech separation with input mixtures like the cocktail party. Prominent methods (e.g., frequency-domain a…