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eess.AS2023
Leveraging In-the-Wild Data for Effective Self-Supervised Pretraining in Speaker Recognition
Shuai Wang, Qibing Bai, Qi Liu +5
Current speaker recognition systems primarily rely on supervised approaches, constrained by the scale of labeled datasets. To boost the system performance, researchers leverage lar…
eess.AS2023
AutoPrep: An Automatic Preprocessing Framework for In-the-Wild Speech Data
Jianwei Yu, Hangting Chen, Yanyao Bian +6
Recently, the utilization of extensive open-sourced text data has significantly advanced the performance of text-based large language models (LLMs). However, the use of in-the-wild…
eess.AS2023
Use of Speech Impairment Severity for Dysarthric Speech Recognition
Mengzhe Geng, Zengrui Jin, Tianzi Wang +7
A key challenge in dysarthric speech recognition is the speaker-level diversity attributed to both speaker-identity associated factors such as gender, and speech impairment severit…