Search Results for author:
Found 9 papers, 4 papers with code
OSUM: Advancing Open Speech Understanding Models with Limited Resources in Academia
Large Language Models (LLMs) have made significant progress in various downstream tasks, inspiring the development of Speech Understanding Language Models (SULMs) to enable comprehensive speech-based interactions.
DQ-Data2vec: Decoupling Quantization for Multilingual Speech Recognition
Data2vec is a self-supervised learning (SSL) approach that employs a teacher-student architecture for contextual representation learning via masked prediction, demonstrating remarkable performance in monolingual ASR.
Ideal-LLM: Integrating Dual Encoders and Language-Adapted LLM for Multilingual Speech-to-Text
Integrating audio encoders with LLMs through connectors has enabled these models to process and comprehend audio modalities, significantly enhancing speech-to-text tasks, including automatic speech recognition (ASR) and automatic speech translation (AST).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
MMGER: Multi-modal and Multi-granularity Generative Error Correction with LLM for Joint Accent and Speech Recognition
Accents represent deviations from standard pronunciation norms, and the multi-task learning framework for simultaneous ASR and accent recognition (AR) has effectively addressed the multi-accent scenarios, making it a prominent solution.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
SSHR: Leveraging Self-supervised Hierarchical Representations for Multilingual Automatic Speech Recognition
Multilingual automatic speech recognition (ASR) systems have garnered attention for their potential to extend language coverage globally.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
TranUSR: Phoneme-to-word Transcoder Based Unified Speech Representation Learning for Cross-lingual Speech Recognition
Different from UniSpeech, UniData2vec replaces the quantized discrete representations with continuous and contextual representations from a teacher model for phonetically-aware pre-training.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
WenetSpeech: A 10000+ Hours Multi-domain Mandarin Corpus for Speech Recognition
In this paper, we present WenetSpeech, a multi-domain Mandarin corpus consisting of 10000+ hours high-quality labeled speech, 2400+ hours weakly labeled speech, and about 10000 hours unlabeled speech, with 22400+ hours in total.
Ranked #6 on
Speech Recognition
on WenetSpeech
Auto-KWS 2021 Challenge: Task, Datasets, and Baselines
Auto-KWS 2021 challenge calls for automated machine learning (AutoML) solutions to automate the process of applying machine learning to a customized keyword spotting task.
The NPU System for the 2020 Personalized Voice Trigger Challenge
This paper describes the system developed by the NPU team for the 2020 personalized voice trigger challenge.
