Literature Review on Automatic Speech Recognition

Abstract

Automatic speech recognition, which was considered to be a concept of science fiction and which has been hit by number of performance degrading factors, is now an important part of information and communication technology. Improvements in the fundamental approaches and development of new approaches by researchers have lead to the advancement of ASRs which were just responding to a set of sounds to sophisticated ASRs which responds to fluently spoken natural language. Using artificial neural networks (ANNs), mathematical models of the low-level circuits in the human brain, to improve speech-recognition performance, through a model known as the ANN-Hidden Markov Model (ANN-HMM) have shown promise for large-vocabulary speech recognition systems. Achieving higher Recognition accuracy, low Word error rate, developing speech corpus depending upon the nature of language and addressing the issues of sources of variability through approaches like Missing Data Techniques & Convolutive Non-Negative Matrix Factorization, are the major considerations for developing an efficient ASR. In this paper, an effort has been made to highlight the progress made so far for ASRs of different languages and the technological perspective of automatic speech recognition in countries like China, Russian,

An Extensive Review of Feature Extraction Techniques, Challenges and Trends in Automatic Speech Recognition

“…In testing stage, the test sample is unknown and the acoustic analysis is performed. In order to discuss the stages involved in ASR architecture [6] [10], it is very important to be aware of the databases that serve as input to the ASR system. Figure 6 shows the architecture of ASR system.…”

Acoustics Speech Processing of Sanskrit Language

“…Wiqas Ghai and Navdeep Singh continued the work further based on the different approaches followed i.e. acoustic-phonetic, pattern recognition, Knowledge Connectionist approach etc [20]. Vadwala et.al.…”

A study of speech recognition system based on the Hidden Markov Model with Gaussian-Mixture

“…Les SRAP comportent deux modules, un module d'extraction des paramètres acoustiques et un module de décodage [5]. Le module d'extraction de paramètres permet de convertir le signal de la parole en des vecteurs acoustiques [6].…”