scholarly journals The IBM 2016 English Conversational Telephone Speech Recognition System

2016 ◽  
Author(s):  
George Saon ◽  
Tom Sercu ◽  
Steven Rennie ◽  
Hong-Kwang J. Kuo
Keyword(s):  
Speech Recognition ◽  
Recognition System ◽  
Speech Recognition System ◽  
Telephone Speech

Robust speech detection method for telephone speech recognition system

1999 ◽  
Vol 27 (2) ◽  
pp. 135-148 ◽  
Author(s):  
Shingo Kuroiwa ◽  
Masaki Naito ◽  
Seiichi Yamamoto ◽  
Norio Higuchi
Keyword(s):  
Speech Recognition ◽  
Detection Method ◽  
Recognition System ◽  
Speech Recognition System ◽  
Speech Detection ◽  
Telephone Speech

The 2004 BBN/LIMSI 20xRT English conversational telephone speech recognition system

2005 ◽  
Author(s):  
Rohit Prasad ◽  
Spyros Matsoukas ◽  
C.-L. Kao ◽  
Jeff Z. Ma ◽  
D.-X. Xu ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Recognition System ◽  
Speech Recognition System ◽  
Telephone Speech

scholarly journals The IBM 2015 English conversational telephone speech recognition system

2015 ◽  
Author(s):  
George Saon ◽  
Hong-Kwang J. Kuo ◽  
Steven Rennie ◽  
Michael Picheny
Keyword(s):  
Speech Recognition ◽  
Recognition System ◽  
Speech Recognition System ◽  
Telephone Speech

Design Of A Voice Controlled Home Automation System Using Deep Learning Convolutional Neural Network (DL-CNN)

2020 ◽  
Vol 8 (1) ◽  
pp. 57-73
Author(s):  
Lery Sakti Ramba
Keyword(s):  
Deep Learning ◽  
Speech Recognition ◽  
Background Noise ◽  
Electronic Devices ◽  
Recognition System ◽  
Background Intensity ◽  
Automation System ◽  
Home Automation ◽  
Speech Recognition System ◽  
Home Automation System

The purpose of this research is to design home automation system that can be controlled using voice commands. This research was conducted by studying other research related to the topics in this research, discussing with competent parties, designing systems, testing systems, and conducting analyzes based on tests that have been done. In this research voice recognition system was designed using Deep Learning Convolutional Neural Networks (DL-CNN). The CNN model that has been designed will then be trained to recognize several kinds of voice commands. The result of this research is a speech recognition system that can be used to control several electronic devices connected to the system. The speech recognition system in this research has a 100% success rate in room conditions with background intensity of 24dB (silent), 67.67% in room conditions with 42dB background noise intensity, and only 51.67% in room conditions with background intensity noise 52dB (noisy). The percentage of the success of the speech recognition system in this research is strongly influenced by the intensity of background noise in a room. Therefore, to obtain optimal results, the speech recognition system in this research is more suitable for use in rooms with low intensity background noise.

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