A Quantitative Approach to Understand Self-Supervised Models as
Cross-lingual Feature Extractors

A Quantitative Approach to Understand Self-Supervised Models as Cross-lingual Feature Extractors

27 November 2023

Shuyue Stella Li

Xiangyu Zhang

Leibny Paola García

Papers citing "A Quantitative Approach to Understand Self-Supervised Models as Cross-lingual Feature Extractors"

5 / 5 papers shown

Title
Description-based Controllable Text-to-Speech with Cross-Lingual Voice Control Ryuichi Yamamoto Yuma Shirahata Masaya Kawamura Kentaro Tachibana DiffM 32 2 0 26 Sep 2024
When LLMs Meets Acoustic Landmarks: An Efficient Approach to Integrate Speech into Large Language Models for Depression Detection Xiangyu Zhang Hexin Liu Kaishuai Xu Qiquan Zhang Daijiao Liu Beena Ahmed Julien Epps 18 7 0 17 Feb 2024
Self-Supervised Speech Representation Learning: A Review Abdel-rahman Mohamed Hung-yi Lee Lasse Borgholt Jakob Drachmann Havtorn Joakim Edin ... Shang-Wen Li Karen Livescu Lars Maaløe Tara N. Sainath Shinji Watanabe SSL AI4TS 124 344 0 21 May 2022
End-to-end Audio-visual Speech Recognition with Conformers Pingchuan Ma Stavros Petridis M. Pantic 79 224 0 12 Feb 2021
Multi-task self-supervised learning for Robust Speech Recognition Mirco Ravanelli Jianyuan Zhong Santiago Pascual P. Swietojanski João Monteiro J. Trmal Yoshua Bengio SSL 171 288 0 25 Jan 2020