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Found 4 papers, 3 papers with code
Leveraging In-the-Wild Data for Effective Self-Supervised Pretraining in Speaker Recognition
Current speaker recognition systems primarily rely on supervised approaches, constrained by the scale of labeled datasets.
A Study of Modeling Rising Intonation in Cantonese Neural Speech Synthesis
In human speech, the attitude of a speaker cannot be fully expressed only by the textual content.
Leveraging Pseudo-labeled Data to Improve Direct Speech-to-Speech Translation
Direct Speech-to-speech translation (S2ST) has drawn more and more attention recently.
LightHuBERT: Lightweight and Configurable Speech Representation Learning with Once-for-All Hidden-Unit BERT
LightHuBERT outperforms the original HuBERT on ASR and five SUPERB tasks with the HuBERT size, achieves comparable performance to the teacher model in most tasks with a reduction of 29% parameters, and obtains a $3. 5\times$ compression ratio in three SUPERB tasks, e. g., automatic speaker verification, keyword spotting, and intent classification, with a slight accuracy loss.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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