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Found 6 papers, 0 papers with code
Target Speech Extraction with Pre-trained Self-supervised Learning Models
We then extend a powerful TSE architecture by incorporating two SSL-based modules: an Adaptive Input Enhancer (AIE) and a speaker encoder.
Probing Self-supervised Learning Models with Target Speech Extraction
TSE uniquely requires both speaker identification and speech separation, distinguishing it from other tasks in the Speech processing Universal PERformance Benchmark (SUPERB) evaluation.
Improving Speaker Verification with Self-Pretrained Transformer Models
Recently, fine-tuning large pre-trained Transformer models using downstream datasets has received a rising interest.
Probing Deep Speaker Embeddings for Speaker-related Tasks
Results show that all deep embeddings encoded channel and content information in addition to speaker identity, but the extent could vary and their performance on speaker-related tasks can be tremendously different: ECAPA-TDNN is dominant in discriminative tasks, and d-vector leads the guiding tasks, while regulating task is less sensitive to the choice of speaker representations.
Parameter-efficient transfer learning of pre-trained Transformer models for speaker verification using adapters
Recently, the pre-trained Transformer models have received a rising interest in the field of speech processing thanks to their great success in various downstream tasks.
An attention-based backend allowing efficient fine-tuning of transformer models for speaker verification
In recent years, self-supervised learning paradigm has received extensive attention due to its great success in various down-stream tasks.
