Search Results for author:
Found 8 papers, 5 papers with code
Hybrid Attention-based Encoder-decoder Model for Efficient Language Model Adaptation
Attention-based encoder-decoder (AED) speech recognition model has been widely successful in recent years.
Adapting Large Language Model with Speech for Fully Formatted End-to-End Speech Recognition
Most end-to-end (E2E) speech recognition models are composed of encoder and decoder blocks that perform acoustic and language modeling functions.
Improving Pseudo-label Training For End-to-end Speech Recognition Using Gradient Mask
In the recent trend of semi-supervised speech recognition, both self-supervised representation learning and pseudo-labeling have shown promising results.
DeCoAR 2.0: Deep Contextualized Acoustic Representations with Vector Quantization
In speech representation learning, a large amount of unlabeled data is used in a self-supervised manner to learn a feature representation.
Deep Contextualized Acoustic Representations For Semi-Supervised Speech Recognition
We propose a novel approach to semi-supervised automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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BERTphone: Phonetically-Aware Encoder Representations for Utterance-Level Speaker and Language Recognition
We introduce BERTphone, a Transformer encoder trained on large speech corpora that outputs phonetically-aware contextual representation vectors that can be used for both speaker and language recognition.
