Experiments With the Acoustic Processor of A Hidden Markov Model-Based, Large Vocabulary Speech Recognition System

1988 ◽  
pp. 533-545 ◽  
Author(s):  
Gerhard Rigoll
Keyword(s):  
Speech Recognition ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Recognition System ◽  
Speech Recognition System ◽  
Large Vocabulary ◽  
Model Based ◽  
Large Vocabulary Speech Recognition

scholarly journals The New Technique Enhancing of Automatic Speech Recognition System for ODIA Language using HTK Based On Hidden Markov Model (HMM)

2020 ◽  
Vol 9 (1) ◽  
pp. 2182-2187
Keyword(s):  
Speech Recognition ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Recognition System ◽  
Speech Recognition System ◽  
Single Word ◽  
Automatic Speech Recognition System ◽  
Java Application ◽  
Regional Language

The purpose of this paper is to address the application to an Indian Regional Language, ODIA of a single word Automatically Speech Recognition System (ASRS). The toolkit is based on Hidden Markov Model (HMM). The details was obtained from 8 ODIA Language speakers. The program is then qualified for 205 different terms in ODIA. Samples from six separate speakers have again been obtained. This is then evaluated in real time. A GUI has been created to enhance the system's interactivity. We used and introduced the test framework for development of the GUI JAVA application. A comprehensive model of an ASR framework was developed to explain each HTK resource using HTK library modules and software. The findings of the experiment indicate that the overall machine efficiency is 93.45%.

scholarly journals Speech Recognition Implementation

2019 ◽  
Vol 8 (9S4) ◽  
pp. 111-116
Keyword(s):  
Speech Recognition ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Automatic Speech Recognition ◽  
Hidden Markov ◽  
Recognition System ◽  
Speech Recognition System ◽  
Automatic Speech Recognition System ◽  
Main Challenge ◽  
Short Time

This paper presents a brief review on Automatic Speech Recognition and provide a technical understanding of ASR system. The objective of this review paper is to elaborate one of the best techniques in the field of speech recognition that is hidden Markov model. Hidden Markov model is very popular technique for speech recognition because speech signal is more like piecewise stationary or short time stationary signal and these models can be trained easily and they are computationally feasible. So, this paper gives a proper implementation of hidden Markov model. After so many years of research, the main challenge in speech recognition field is accuracy. The speech recognition system includes feature extraction, building word template, comparing word and selecting the best with maximum likelihood. Hence, this paper will give a great contribution for understanding the concepts of Automatic Speech Recognition system and hidden Markov model.

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