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Found 7 papers, 3 papers with code
Text-Only Domain Adaptation for End-to-End Speech Recognition through Down-Sampling Acoustic Representation
Mapping two modalities, speech and text, into a shared representation space, is a research topic of using text-only data to improve end-to-end automatic speech recognition (ASR) performance in new domains.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
3M: Multi-loss, Multi-path and Multi-level Neural Networks for speech recognition
Recently, Conformer based CTC/AED model has become a mainstream architecture for ASR.
Ranked #2 on
Speech Recognition
on WenetSpeech
SpeechMoE2: Mixture-of-Experts Model with Improved Routing
Mixture-of-experts based acoustic models with dynamic routing mechanisms have proved promising results for speech recognition.
GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audio
This paper introduces GigaSpeech, an evolving, multi-domain English speech recognition corpus with 10, 000 hours of high quality labeled audio suitable for supervised training, and 40, 000 hours of total audio suitable for semi-supervised and unsupervised training.
Ranked #1 on
Speech Recognition
on GigaSpeech
SpeechMoE: Scaling to Large Acoustic Models with Dynamic Routing Mixture of Experts
Recently, Mixture of Experts (MoE) based Transformer has shown promising results in many domains.
DFSMN-SAN with Persistent Memory Model for Automatic Speech Recognition
Self-attention networks (SAN) have been introduced into automatic speech recognition (ASR) and achieved state-of-the-art performance owing to its superior ability in capturing long term dependency.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Teach an all-rounder with experts in different domains
First, for each domain, a teacher model (domain-dependent model) is trained by fine-tuning a multi-condition model with domain-specific subset.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
