Search Results for author:
Found 7 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.
Extracting speaker and emotion information from self-supervised speech models via channel-wise correlations
Self-supervised learning of speech representations from large amounts of unlabeled data has enabled state-of-the-art results in several speech processing 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.
Analyzing speaker verification embedding extractors and back-ends under language and channel mismatch
In this paper, we analyze the behavior and performance of speaker embeddings and the back-end scoring model under domain and language mismatch.
Self-supervised speaker embeddings
Contrary to i-vectors, speaker embeddings such as x-vectors are incapable of leveraging unlabelled utterances, due to the classification loss over training speakers.
AUTOMATIC LANGUAGE IDENTIFICATION USING DEEP NEURAL NETWORKS
This work studies the use of deep neural networks (DNNs) to address automatic language identification (LID).
