activity
20172025
most citedSUPERB: Speech processing Universal PERformance Benchmark

53 citations · 118 across the 15 of their papers we have counts for

collaborators

17 papers

eess.AS2025

Factorized RVQ-GAN For Disentangled Speech Tokenization

Sameer Khurana, Dominik Klement, Antoine Laurent +13

We propose Hierarchical Audio Codec (HAC), a unified neural speech codec that factorizes its bottleneck into three linguistic levels-acoustic, phonetic, and lexical-within a single…

eess.AS2024★ 1 cited

A Large-Scale Evaluation of Speech Foundation Models

Shu-wen Yang, Heng-Jui Chang, Zili Huang +18

The foundation model paradigm leverages a shared foundation model to achieve state-of-the-art (SOTA) performance for various tasks, requiring minimal downstream-specific modeling a…

eess.AS2023

UniX-Encoder: A Universal -Channel Speech Encoder for Ad-Hoc Microphone Array Speech Processing

Zili Huang, Yiwen Shao, Shi-Xiong Zhang +1

The speech field is evolving to solve more challenging scenarios, such as multi-channel recordings with multiple simultaneous talkers. Given the many types of microphone setups out…

eess.AS2022

Self-supervised learning with bi-label masked speech prediction for streaming multi-talker speech recognition

Zili Huang, Zhuo Chen, Naoyuki Kanda +6

Self-supervised learning (SSL), which utilizes the input data itself for representation learning, has achieved state-of-the-art results for various downstream speech tasks. However…

eess.AS2022★ 2 cited

Adapting self-supervised models to multi-talker speech recognition using speaker embeddings

Zili Huang, Desh Raj, Paola García +1

Self-supervised learning (SSL) methods which learn representations of data without explicit supervision have gained popularity in speech-processing tasks, particularly for single-t…

cs.CL2022

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…

eess.AS2022

Investigating self-supervised learning for speech enhancement and separation

Zili Huang, Shinji Watanabe, Shu-wen Yang +2

Speech enhancement and separation are two fundamental tasks for robust speech processing. Speech enhancement suppresses background noise while speech separation extracts target spe…

cs.CL2022★ 3 cited

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…

eess.AS2021★ 2 cited

Target-speaker Voice Activity Detection with Improved I-Vector Estimation for Unknown Number of Speaker

Maokui He, Desh Raj, Zili Huang +3

Target-speaker voice activity detection (TS-VAD) has recently shown promising results for speaker diarization on highly overlapped speech. However, the original model requires a fi…

cs.CL2021★ 53 cited

SUPERB: Speech processing Universal PERformance Benchmark

Shu-wen Yang, Po-Han Chi, Yung-Sung Chuang +17

Self-supervised learning (SSL) has proven vital for advancing research in natural language processing (NLP) and computer vision (CV). The paradigm pretrains a shared model on large…

eess.AS2021★ 27 cited

The Hitachi-JHU DIHARD III System: Competitive End-to-End Neural Diarization and X-Vector Clustering Systems Combined by DOVER-Lap

Shota Horiguchi, Nelson Yalta, Paola Garcia +7

This paper provides a detailed description of the Hitachi-JHU system that was submitted to the Third DIHARD Speech Diarization Challenge. The system outputs the ensemble results of…

eess.AS2020

Multi-class Spectral Clustering with Overlaps for Speaker Diarization

Desh Raj, Zili Huang, Sanjeev Khudanpur

This paper describes a method for overlap-aware speaker diarization. Given an overlap detector and a speaker embedding extractor, our method performs spectral clustering of segment…

eess.AS2020

Integration of speech separation, diarization, and recognition for multi-speaker meetings: System description, comparison, and analysis

Desh Raj, Pavel Denisov, Zhuo Chen +11

Multi-speaker speech recognition of unsegmented recordings has diverse applications such as meeting transcription and automatic subtitle generation. With technical advances in syst…

eess.AS2020

DOVER-Lap: A Method for Combining Overlap-aware Diarization Outputs

Desh Raj, Leibny Paola Garcia-Perera, Zili Huang +4

Several advances have been made recently towards handling overlapping speech for speaker diarization. Since speech and natural language tasks often benefit from ensemble techniques…

eess.AS2020★ 2 cited

Speaker Diarization with Region Proposal Network

Zili Huang, Shinji Watanabe, Yusuke Fujita +4

Speaker diarization is an important pre-processing step for many speech applications, and it aims to solve the "who spoke when" problem. Although the standard diarization systems c…

cs.SD2018★ 3 cited

Deep Discriminant Analysis for i-vector Based Robust Speaker Recognition

Shuai Wang, Zili Huang, Yanmin Qian +1

Linear Discriminant Analysis (LDA) has been used as a standard post-processing procedure in many state-of-the-art speaker recognition tasks. Through maximizing the inter-speaker di…

cs.LG2017★ 25 cited

Recover Missing Sensor Data with Iterative Imputing Network

Jingguang Zhou, Zili Huang

Sensor data has been playing an important role in machine learning tasks, complementary to the human-annotated data that is usually rather costly. However, due to systematic or acc…