scholarly journals Speech Recognition System of Slovenian Broadcast News

10.5772/17161 ◽  
2011 ◽  
Author(s):  
Mirjam Sepesy ◽  
Andrej Zgank
Keyword(s):  
Speech Recognition ◽  
Recognition System ◽  
Speech Recognition System ◽  
Broadcast News

Cross-task portability of a broadcast news speech recognition system

2002 ◽  
Vol 38 (3-4) ◽  
pp. 335-347 ◽  
Author(s):  
N. Bertoldi ◽  
F. Brugnara ◽  
M. Cettolo ◽  
M. Federico ◽  
D. Giuliani
Keyword(s):  
Speech Recognition ◽  
Recognition System ◽  
Speech Recognition System ◽  
Broadcast News

Arabic Phonetic Dictionaries for Speech Recognition

2009 ◽  
Vol 2 (4) ◽  
pp. 67-80 ◽  
Author(s):  
Mohamed Ali ◽  
Moustafa Elshafei ◽  
Mansour Al-Ghamdi ◽  
Husni Al-Muhtaseb
Keyword(s):  
Speech Recognition ◽  
Language Model ◽  
Recognition System ◽  
Speech Recognition System ◽  
Broadcast News ◽  
Word Error Rate ◽  
Large Vocabulary ◽  
Essential Components ◽  
News Corpus ◽  
Arabic Speech Recognition

Phonetic dictionaries are essential components of large-vocabulary speaker-independent speech recognition systems. This paper presents a rule-based technique to generate phonetic dictionaries for a large vocabulary Arabic speech recognition system. The system used conventional Arabic pronunciation rules, common pronunciation rules of Modern Standard Arabic, as well as some common dialectal cases. The paper gives in detail an explanation of these rules as well as their formal mathematical presentation. The rules were used to generate a dictionary for a 5.4 hour corpus of broadcast news. The rules and the phone set were tested and evaluated on an Arabic speech recognition system. The system was trained on 4.3 hours of the 5.4 hours of Arabic broadcast news corpus and tested on the remaining 1.1 hours. The phonetic dictionary contains 23,841 definitions corresponding to about 14232 words. The language model contains both bi-grams and tri-grams. The Word Error Rate (WER) came to 9.0%.

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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