Vowel place detection for a knowledge‐based speech recognition system

2008 ◽  
Vol 123 (5) ◽  
pp. 3330-3330
Author(s):  
Sukmyung Lee ◽  
Jeung‐Yoon Choi
Keyword(s):  
Speech Recognition ◽  
Recognition System ◽  
Speech Recognition System ◽  
Knowledge Based

scholarly journals Consonant Voicing Landmark Detection

2019 ◽  
Author(s):  
はなさき
Keyword(s):  
Speech Recognition ◽  
Recognition System ◽  
Extensive Study ◽  
Speech Recognition System ◽  
Acoustic Parameters ◽  
Landmark Detection ◽  
Knowledge Based ◽  
Consonant Voicing

The landmark detection is the first step of a knowledge-based speech recognition system. For landmark detection, it is necessary to extract some useful landmark acoustic parameters. In this project, we do a extensive study about consonant voicing landmark detection.

scholarly journals Handling within-word and cross-word pronunciation variation for Arabic speech recognition (knowledge-based approach)

2020 ◽  
pp. 72-79
Author(s):  
Ibrahim El El-Henawy ◽  
◽  
◽  
Marwa Abo Abo-Elazm
Keyword(s):  
Speech Recognition ◽  
Recognition Performance ◽  
Recognition System ◽  
Speech Recognition System ◽  
Automatic Speech Recognition System ◽  
Pronunciation Variation ◽  
Knowledge Based ◽  
Data Driven Approach ◽  
Using Data ◽  
Phonological Rules

Arabic is one of the phonetically complex languages, and the creation of accurate speech recognition system is a challengeable task. Phonetic dictionary is essential component in automatic speech recognition system (ASR). The pronunciation variations in Arabic are tangible and are investigated widely using data driven approach or knowledge based approach. The phonological rules are used to get the pronunciation of each word accurately to reduce the mismatch between the actual phoneme representation of the spoken words and ASR dictionary. Several studies in Arabic ASR system are conducted using different number of phonological rules. In this paper we focus on those rule that handle within-word pronunciation variation and cross-word pronunciation variation. The experimental results indicate that handling within-word pronunciation variation using phonological rule doesn’t enhance the recognition performance, but using these rules to handle cross-word variation provide a good performance.

Development of HMM/Neural Network‐Based Medium‐Vocabulary Isolated‐Word Lithuanian Speech Recognition System

Informatica ◽  
2004 ◽  
Vol 15 (4) ◽  
pp. 465-474 ◽  
Author(s):  
Mark Filipovič ◽  
Antanas Lipeika
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Recognition System ◽  
Speech Recognition System ◽  
Isolated Word

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.

Development of Assamese Continuous Speech Recognition System

2018 ◽  
Author(s):  
Barsha Deka ◽  
Nirmala S.R. ◽  
Samudravijaya K.
Keyword(s):  
Speech Recognition ◽  
Recognition System ◽  
Speech Recognition System ◽  
Continuous Speech ◽  
Continuous Speech Recognition

TDNN-based Multilingual Speech Recognition System for Low Resource Indian Languages

2018 ◽  
Author(s):  
Noor Fathima ◽  
Tanvina Patel ◽  
Mahima C ◽  
Anuroop Iyengar
Keyword(s):  
Speech Recognition ◽  
Recognition System ◽  
Speech Recognition System ◽  
Indian Languages ◽  
Low Resource ◽  
Multilingual Speech Recognition

Improving Large Vocabulary Urdu Speech Recognition System Using Deep Neural Networks

2019 ◽  
Author(s):  
Muhammad Umar Farooq ◽  
Farah Adeeba ◽  
Sahar Rauf ◽  
Sarmad Hussain
Keyword(s):  
Neural Networks ◽  
Speech Recognition ◽  
Deep Neural Networks ◽  
Recognition System ◽  
Speech Recognition System ◽  
Large Vocabulary

Development of Large Vocabulary Speech Recognition System with Keyword Search for Manipuri

2018 ◽  
Author(s):  
Tanvina Patel ◽  
Krishna D N ◽  
Noor Fathima ◽  
Nisar Shah ◽  
Mahima C ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Keyword Search ◽  
Recognition System ◽  
Speech Recognition System ◽  
Large Vocabulary ◽  
Large Vocabulary Speech Recognition
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