research
∙
05/18/2023
A Comparative Study on E-Branchformer vs Conformer in Speech Recognition, Translation, and Understanding Tasks
Conformer, a convolution-augmented Transformer variant, has become the d...
share
research
∙
02/27/2023
Structured Pruning of Self-Supervised Pre-trained Models for Speech Recognition and Understanding
Self-supervised speech representation learning (SSL) has shown to be eff...
research
∙
12/16/2022
Context-aware Fine-tuning of Self-supervised Speech Models
Self-supervised pre-trained transformers have improved the state of the ...
research
∙
09/30/2022
E-Branchformer: Branchformer with Enhanced merging for speech recognition
Conformer, combining convolution and self-attention sequentially to capt...
research
∙
05/02/2022
Wav2Seq: Pre-training Speech-to-Text Encoder-Decoder Models Using Pseudo Languages
We introduce Wav2Seq, the first self-supervised approach to pre-train bo...
research
∙
10/11/2021
SRU++: Pioneering Fast Recurrence with Attention for Speech Recognition
The Transformer architecture has been well adopted as a dominant archite...
research
∙
09/14/2021
Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition
This paper is a study of performance-efficiency trade-offs in pre-traine...
research
∙
06/17/2021
Multi-mode Transformer Transducer with Stochastic Future Context
Automatic speech recognition (ASR) models make fewer errors when more su...
research
∙
07/23/2020
Sequential Routing Framework: Fully Capsule Network-based Speech Recognition
Capsule networks (CapsNets) have recently gotten attention as alternativ...
research
∙
02/15/2020
Small energy masking for improved neural network training for end-to-end speech recognition
In this paper, we present a Small Energy Masking (SEM) algorithm, which ...
research
∙
01/02/2020
Attention based on-device streaming speech recognition with large speech corpus
In this paper, we present a new on-device automatic speech recognition (...
research
∙
12/28/2019
Improved Multi-Stage Training of Online Attention-based Encoder-Decoder Models
In this paper, we propose a refined multi-stage multi-task training stra...
research
∙
12/22/2019
power-law nonlinearity with maximally uniform distribution criterion for improved neural network training in automatic speech recognition
In this paper, we describe the Maximum Uniformity of Distribution (MUD) ...
research
∙
12/22/2019
