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Found 11 papers, 2 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.
DiaCorrect: Error Correction Back-end For Speaker Diarization
In this work, we propose an error correction framework, named DiaCorrect, to refine the output of a diarization system in a simple yet effective way.
End-to-End Open Vocabulary Keyword Search With Multilingual Neural Representations
Conventional keyword search systems operate on automatic speech recognition (ASR) outputs, which causes them to have a complex indexing and search pipeline.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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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.
DPCCN: Densely-Connected Pyramid Complex Convolutional Network for Robust Speech Separation And Extraction
Particularly, we find that the Mixture-Remix fine-tuning with DPCCN significantly outperforms the TD-SpeakerBeam for unsupervised cross-domain TSE, with around 3. 5 dB SISNR improvement on target domain test set, without any source domain performance degradation.
A Hierarchical Subspace Model for Language-Attuned Acoustic Unit Discovery
In the target language, we infer both the language and unit embeddings in an unsupervised manner, and in so doing, we simultaneously learn a subspace of units specific to that language and the units that dwell on it.
A Technical Report: BUT Speech Translation Systems
To reduce this performance degradation, we have jointly-trained ASR and MT modules with ASR objective as an auxiliary loss.
How to Improve Your Speaker Embeddings Extractor in Generic Toolkits
Recently, speaker embeddings extracted with deep neural networks became the state-of-the-art method for speaker verification.
Spoken Pass-Phrase Verification in the i-vector Space
The task of spoken pass-phrase verification is to decide whether a test utterance contains the same phrase as given enrollment utterances.
