Search Results for author:
Found 5 papers, 2 papers with code
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.
Latency-Controlled Neural Architecture Search for Streaming Speech Recognition
Extensive experiments show that: 1) Based on the proposed neural architecture, the neural networks with a medium latency of 550ms (millisecond) and a low latency of 190ms can be learned in the vanilla and revised operation space respectively.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
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.
Learning discriminative features in sequence training without requiring framewise labelled data
We propose a novel method which simultaneously models both the sequence discriminative training and the feature discriminative learning within a single network architecture, so that it can learn discriminative deep features in sequence training that obviates the need for presegmented training data.
