Search Results for author:
Found 6 papers, 4 papers with code
CUSIDE: Chunking, Simulating Future Context and Decoding for Streaming ASR
The simulation module is jointly trained with the ASR model using a self-supervised loss; the ASR model is optimized with the usual ASR loss, e. g., CTC-CRF as used in our experiments.
An Empirical Study of Language Model Integration for Transducer based Speech Recognition
Based on the DR method, we propose a low-order density ratio method (LODR) by replacing the estimation with a low-order weak language model.
Multilingual and crosslingual speech recognition using phonological-vector based phone embeddings
The use of phonological features (PFs) potentially allows language-specific phones to remain linked in training, which is highly desirable for information sharing for multilingual and crosslingual speech recognition methods for low-resourced languages.
Advancing CTC-CRF Based End-to-End Speech Recognition with Wordpieces and Conformers
Automatic speech recognition systems have been largely improved in the past few decades and current systems are mainly hybrid-based and end-to-end-based.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Efficient Neural Architecture Search for End-to-end Speech Recognition via Straight-Through Gradients
Using ST gradients to support sub-graph sampling is a core element to achieve efficient NAS beyond DARTS and SNAS.
Ranked #1 on
Speech Recognition
on WSJ dev93
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
An empirical study of domain-agnostic semi-supervised learning via energy-based models: joint-training and pre-training
In contrast, generative SSL methods involve unsupervised learning based on generative models by either joint-training or pre-training, and are more appealing from the perspective of being domain-agnostic, since they do not inherently require data augmentations.
