Search Results for author:
Found 10 papers, 6 papers with code
NEST-RQ: Next Token Prediction for Speech Self-Supervised Pre-Training
NEST-RQ employs causal encoders with only left context and uses next token prediction (NTP) as the training task.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Seed-ASR: Understanding Diverse Speech and Contexts with LLM-based Speech Recognition
Modern automatic speech recognition (ASR) model is required to accurately transcribe diverse speech signals (from different domains, languages, accents, etc) given the specific contextual information in various application scenarios.
Ranked #2 on
Speech Recognition
on AISHELL-1
(using extra training data)
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
VILAS: Exploring the Effects of Vision and Language Context in Automatic Speech Recognition
In this paper, we first propose ViLaS (Vision and Language into Automatic Speech Recognition), a novel multimodal ASR model based on the continuous integrate-and-fire (CIF) mechanism, which can integrate visual and textual context simultaneously or separately, to facilitate speech recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
X-LLM: Bootstrapping Advanced Large Language Models by Treating Multi-Modalities as Foreign Languages
(3) Integrating multiple modalities: all single-modal encoders are aligned with the LLM through X2L interfaces to integrate multimodal capabilities into the LLM.
Matching-based Term Semantics Pre-training for Spoken Patient Query Understanding
Medical Slot Filling (MSF) task aims to convert medical queries into structured information, playing an essential role in diagnosis dialogue systems.
Complex Dynamic Neurons Improved Spiking Transformer Network for Efficient Automatic Speech Recognition
The spiking neural network (SNN) using leaky-integrated-and-fire (LIF) neurons has been commonly used in automatic speech recognition (ASR) tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Knowledge Transfer from Pre-trained Language Models to Cif-based Speech Recognizers via Hierarchical Distillation
Large-scale pre-trained language models (PLMs) have shown great potential in natural language processing tasks.
Ranked #8 on
Speech Recognition
on AISHELL-1
Improving End-to-End Contextual Speech Recognition with Fine-Grained Contextual Knowledge Selection
Nowadays, most methods in end-to-end contextual speech recognition bias the recognition process towards contextual knowledge.
CIF-based Collaborative Decoding for End-to-end Contextual Speech Recognition
End-to-end (E2E) models have achieved promising results on multiple speech recognition benchmarks, and shown the potential to become the mainstream.
