scholarly journals Harpy, a connected speech recognition system

1976 ◽  
Vol 59 (S1) ◽  
pp. S97-S97 ◽  
Author(s):  
Bruce P. Lowerre ◽  
B. Raj Reddy
Keyword(s):  
Speech Recognition ◽  
Recognition System ◽  
Speech Recognition System ◽  
Connected Speech

The Use of Word, Phrase and Intent Accuracy as Measures of Connected Speech Recognition Performance

1994 ◽  
Vol 38 (4) ◽  
pp. 325-329
Author(s):  
Tim Barry ◽  
Tom Solz ◽  
John Reising ◽  
Dave Williamson
Keyword(s):  
Speech Recognition ◽  
Recognition Performance ◽  
Recognition System ◽  
Speech Recognition System ◽  
Connected Speech

Eleven subjects participated in a study designed to test the accuracy of a newer-generation connected speech recognition system using a 49 word vocabulary likely to be used in an aircraft cockpit environment. The 49 vocabulary words were used to create 392 test phrases. These phrases were divided into three groups: Complex phrases, which contain more than five words, and two groups of Simple phrases, which contain 5 words or less. The simple phrases were divided into Simple Alternate and Simple No-Alternate phrases, depending on whether or not the phrase was the only one in the entire vocabulary capable of carrying out a particular action once recognition occurred. Performance of the recognition system was measured with three accuracy statistics: word accuracy, the most commonly reported statistic in speech recognition research, phrase accuracy, which is gaining popularity in connected speech recognition research, and intent accuracy, which is probably the most relevant statistic that could be reported in research of this type. Significantly different word, phrase, and intent accuracy results were obtained for the three different phrase types.

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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