216 citations · 556 across the 81 of their papers we have counts for
9 papers · 1 filter
GPU-accelerated Guided Source Separation for Meeting Transcription
Desh Raj, Daniel Povey, Sanjeev Khudanpur
Guided source separation (GSS) is a type of target-speaker extraction method that relies on pre-computed speaker activities and blind source separation to perform front-end enhance…
EURO: ESPnet Unsupervised ASR Open-source Toolkit
Dongji Gao, Jiatong Shi, Shun-Po Chuang +4
This paper describes the ESPnet Unsupervised ASR Open-source Toolkit (EURO), an end-to-end open-source toolkit for unsupervised automatic speech recognition (UASR). EURO adopts the…
Adapting self-supervised models to multi-talker speech recognition using speaker embeddings
Zili Huang, Desh Raj, Paola García +1
Self-supervised learning (SSL) methods which learn representations of data without explicit supervision have gained popularity in speech-processing tasks, particularly for single-t…
Reducing Language confusion for Code-switching Speech Recognition with Token-level Language Diarization
Hexin Liu, Haihua Xu, Leibny Paola Garcia +3
Code-switching (CS) refers to the phenomenon that languages switch within a speech signal and leads to language confusion for automatic speech recognition (ASR). This paper aims to…
Defense against Adversarial Attacks on Hybrid Speech Recognition using Joint Adversarial Fine-tuning with Denoiser
Sonal Joshi, Saurabh Kataria, Yiwen Shao +4
Adversarial attacks are a threat to automatic speech recognition (ASR) systems, and it becomes imperative to propose defenses to protect them. In this paper, we perform experiments…
PHO-LID: A Unified Model Incorporating Acoustic-Phonetic and Phonotactic Information for Language Identification
Hexin Liu, Leibny Paola Garcia Perera, Andy W. H. Khong +2
We propose a novel model to hierarchically incorporate phoneme and phonotactic information for language identification (LID) without requiring phoneme annotations for training. In…
Investigating self-supervised learning for speech enhancement and separation
Zili Huang, Shinji Watanabe, Shu-wen Yang +2
Speech enhancement and separation are two fundamental tasks for robust speech processing. Speech enhancement suppresses background noise while speech separation extracts target spe…
Enhance Language Identification using Dual-mode Model with Knowledge Distillation
Hexin Liu, Leibny Paola Garcia Perera, Andy W. H. Khong +3
In this paper, we propose to employ a dual-mode framework on the x-vector self-attention (XSA-LID) model with knowledge distillation (KD) to enhance its language identification (LI…
Textual Data Augmentation for Arabic-English Code-Switching Speech Recognition
Amir Hussein, Shammur Absar Chowdhury, Ahmed Abdelali +3
The pervasiveness of intra-utterance code-switching (CS) in spoken content requires that speech recognition (ASR) systems handle mixed language. Designing a CS-ASR system has many…