Investigating self-supervised learning for speech enhancement and separation

no code implementations • 15 Mar 2022 • Zili Huang, Shinji Watanabe, Shu-wen Yang, Paola Garcia, Sanjeev Khudanpur

Speech enhancement and separation are two fundamental tasks for robust speech processing.

Self-Supervised Learning Speech Enhancement +1

SUPERB-SG: Enhanced Speech processing Universal PERformance Benchmark for Semantic and Generative Capabilities

1 code implementation • ACL 2022 • Hsiang-Sheng Tsai, Heng-Jui Chang, Wen-Chin Huang, Zili Huang, Kushal Lakhotia, Shu-wen Yang, Shuyan Dong, Andy T. Liu, Cheng-I Jeff Lai, Jiatong Shi, Xuankai Chang, Phil Hall, Hsuan-Jui Chen, Shang-Wen Li, Shinji Watanabe, Abdelrahman Mohamed, Hung-Yi Lee

In this paper, we introduce SUPERB-SG, a new benchmark focused on evaluating the semantic and generative capabilities of pre-trained models by increasing task diversity and difficulty over SUPERB.

Self-Supervised Learning Transfer Learning

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

no code implementations • 7 Aug 2021 • Maokui He, Desh Raj, Zili Huang, Jun Du, Zhuo Chen, Shinji Watanabe

Target-speaker voice activity detection (TS-VAD) has recently shown promising results for speaker diarization on highly overlapped speech.

Action Detection Activity Detection +3

SUPERB: Speech processing Universal PERformance Benchmark

3 code implementations • 3 May 2021 • Shu-wen Yang, Po-Han Chi, Yung-Sung Chuang, Cheng-I Jeff Lai, Kushal Lakhotia, Yist Y. Lin, Andy T. Liu, Jiatong Shi, Xuankai Chang, Guan-Ting Lin, Tzu-Hsien Huang, Wei-Cheng Tseng, Ko-tik Lee, Da-Rong Liu, Zili Huang, Shuyan Dong, Shang-Wen Li, Shinji Watanabe, Abdelrahman Mohamed, Hung-Yi Lee

SUPERB is a leaderboard to benchmark the performance of a shared model across a wide range of speech processing tasks with minimal architecture changes and labeled data.

Representation Learning Self-Supervised Learning

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

no code implementations • 2 Feb 2021 • Shota Horiguchi, Nelson Yalta, Paola Garcia, Yuki Takashima, Yawen Xue, Desh Raj, Zili Huang, Yusuke Fujita, Shinji Watanabe, Sanjeev Khudanpur

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

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

no code implementations • 3 Nov 2020 • Desh Raj, Pavel Denisov, Zhuo Chen, Hakan Erdogan, Zili Huang, Maokui He, Shinji Watanabe, Jun Du, Takuya Yoshioka, Yi Luo, Naoyuki Kanda, Jinyu Li, Scott Wisdom, John R. Hershey

Multi-speaker speech recognition of unsegmented recordings has diverse applications such as meeting transcription and automatic subtitle generation.

Automatic Speech Recognition Speaker Diarization +1

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

1 code implementation • 3 Nov 2020 • Desh Raj, Leibny Paola Garcia-Perera, Zili Huang, Shinji Watanabe, Daniel Povey, Andreas Stolcke, Sanjeev Khudanpur

Several advances have been made recently towards handling overlapping speech for speaker diarization.

Audio and Speech Processing Sound

Speaker Diarization with Region Proposal Network

1 code implementation • 14 Feb 2020 • Zili Huang, Shinji Watanabe, Yusuke Fujita, Paola Garcia, Yiwen Shao, Daniel Povey, Sanjeev Khudanpur

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

Region Proposal Speaker Diarization

Recover Missing Sensor Data with Iterative Imputing Network

no code implementations • 20 Nov 2017 • 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.