Search Results for author:
Found 10 papers, 1 papers with code
Self-regularised Minimum Latency Training for Streaming Transformer-based Speech Recognition
In previous works, latency was optimised by truncating the online attention weights based on the hard alignments obtained from conventional ASR models, without taking into account the potential loss of ASR accuracy.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Speaker Reinforcement Using Target Source Extraction for Robust Automatic Speech Recognition
Improving the accuracy of single-channel automatic speech recognition (ASR) in noisy conditions is challenging.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
On monoaural speech enhancement for automatic recognition of real noisy speech using mixture invariant training
In this paper, we explore an improved framework to train a monoaural neural enhancement model for robust speech recognition.
Transformer-based Streaming ASR with Cumulative Attention
In this paper, we propose an online attention mechanism, known as cumulative attention (CA), for streaming Transformer-based automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Monaural source separation: From anechoic to reverberant environments
Impressive progress in neural network-based single-channel speech source separation has been made in recent years.
Teacher-Student MixIT for Unsupervised and Semi-supervised Speech Separation
The proposed method first uses mixtures of unseparated sources and the mixture invariant training (MixIT) criterion to train a teacher model.
Head-synchronous Decoding for Transformer-based Streaming ASR
Online Transformer-based automatic speech recognition (ASR) systems have been extensively studied due to the increasing demand for streaming applications.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Time-Domain Speech Extraction with Spatial Information and Multi Speaker Conditioning Mechanism
In this paper, we present a novel multi-channel speech extraction system to simultaneously extract multiple clean individual sources from a mixture in noisy and reverberant environments.
On End-to-end Multi-channel Time Domain Speech Separation in Reverberant Environments
To reduce the influence of reverberation on spatial feature extraction, a dereverberation pre-processing method has been applied to further improve the separation performance.
An Investigation into the Effectiveness of Enhancement in ASR Training and Test for CHiME-5 Dinner Party Transcription
Despite the strong modeling power of neural network acoustic models, speech enhancement has been shown to deliver additional word error rate improvements if multi-channel data is available.
