Next Article in Journal / Special Issue
Satellite IoT Edge Intelligent Computing: A Research on Architecture
Previous Article in Journal
Broadband High-Gain Antenna for Millimetre-Wave 60-GHz Band
Previous Article in Special Issue
Multi-Source Reliable Multicast Routing with QoS Constraints of NFV in Edge Computing
Open AccessArticle

Post Text Processing of Chinese Speech Recognition Based on Bidirectional LSTM Networks and CRF

by Li Yang 1, Ying Li 1, Jin Wang 1,2,* and Zhuo Tang 3,4
1
Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, School of Computer & Communication Engineering, Changsha University of Science & Technology, Changsha 410004, China
2
School of Information Science and Engineering, Fujian University of Technology, Fuzhou 350118, China
3
College of Information Science and Engineering, Hunan University, Changsha 410082, China
4
National Supercomputing Center in Changsha, Hunan University, Changsha 410082, China
*
Author to whom correspondence should be addressed.
Electronics 2019, 8(11), 1248; https://doi.org/10.3390/electronics8111248
Received: 28 September 2019 / Revised: 23 October 2019 / Accepted: 28 October 2019 / Published: 31 October 2019
(This article belongs to the Special Issue AI Enabled Communication on IoT Edge Computing)
With the rapid development of Internet of Things Technology, speech recognition has been applied more and more widely. Chinese Speech Recognition is a complex process. In the process of speech-to-text conversion, due to the influence of dialect, environmental noise, and context, the accuracy of speech-to-text in multi-round dialogues and specific contexts is still not high. After the general speech recognition technology, the text after speech recognition can be detected and corrected in the specific context, which is helpful to improve the robustness of text comprehension and is a beneficial supplement to the speech recognition technology. In this paper, a text processing model after Chinese Speech Recognition is proposed, which combines a bidirectional long short-term memory (LSTM) network with a conditional random field (CRF) model. The task is divided into two stages: text error detection and text error correction. In this paper, a bidirectional long short-term memory (Bi-LSTM) network and conditional random field are used in two stages of text error detection and text error correction respectively. Through verification and system test on the SIGHAN 2013 Chinese Spelling Check (CSC) dataset, the experimental results show that the model can effectively improve the accuracy of text after speech recognition. View Full-Text
Keywords: error detection; error correction; LSTM; CRF; Chinese speech recognition error detection; error correction; LSTM; CRF; Chinese speech recognition
Show Figures

Figure 1

MDPI and ACS Style

Yang, L.; Li, Y.; Wang, J.; Tang, Z. Post Text Processing of Chinese Speech Recognition Based on Bidirectional LSTM Networks and CRF. Electronics 2019, 8, 1248.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop