Search Results for author:
Found 9 papers, 1 papers with code
Two-Stage Augmentation and Adaptive CTC Fusion for Improved Robustness of Multi-Stream End-to-End ASR
Performance degradation of an Automatic Speech Recognition (ASR) system is commonly observed when the test acoustic condition is different from training.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
A practical two-stage training strategy for multi-stream end-to-end speech recognition
The multi-stream paradigm of audio processing, in which several sources are simultaneously considered, has been an active research area for information fusion.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Multi-Stream End-to-End Speech Recognition
Two representative framework have been proposed and discussed, which are Multi-Encoder Multi-Resolution (MEM-Res) framework and Multi-Encoder Multi-Array (MEM-Array) framework, respectively.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Performance Monitoring for End-to-End Speech Recognition
Measuring performance of an automatic speech recognition (ASR) system without ground-truth could be beneficial in many scenarios, especially with data from unseen domains, where performance can be highly inconsistent.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Exploring Methods for the Automatic Detection of Errors in Manual Transcription
Quality of data plays an important role in most deep learning tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Multi-encoder multi-resolution framework for end-to-end speech recognition
In this work, we present a novel Multi-Encoder Multi-Resolution (MEMR) framework based on the joint CTC/Attention model.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Stream attention-based multi-array end-to-end speech recognition
Automatic Speech Recognition (ASR) using multiple microphone arrays has achieved great success in the far-field robustness.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Multilingual sequence-to-sequence speech recognition: architecture, transfer learning, and language modeling
In this work, we attempt to use data from 10 BABEL languages to build a multi-lingual seq2seq model as a prior model, and then port them towards 4 other BABEL languages using transfer learning approach.
Language Modelling
Sequence-To-Sequence Speech Recognition
+2
Large-scale Artificial Neural Network: MapReduce-based Deep Learning
Faced with continuously increasing scale of data, original back-propagation neural network based machine learning algorithm presents two non-trivial challenges: huge amount of data makes it difficult to maintain both efficiency and accuracy; redundant data aggravates the system workload.
