Adapting automatic speech recognition methods to speech perception: A hidden semi‐Markov model of listener’s categorization of a VC(C)V continuum

2004 ◽  
Vol 116 (4) ◽  
pp. 2570-2571
Author(s):  
Terrance M. Nearey
Keyword(s):  
Speech Recognition ◽  
Speech Perception ◽  
Markov Model ◽  
Automatic Speech Recognition

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.

scholarly journals Comparison of Hidden Markov Model and Recurrent Neural Network in Automatic Speech Recognition

2020 ◽  
Vol 5 (8) ◽  
pp. 958-965
Author(s):  
Akshay Madhav Deshmukh
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Speech Recognition ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Automatic Speech Recognition ◽  
Hidden Markov ◽  
Multiple Resources ◽  
Significant Research ◽  
Advance Research

Understanding human speech precisely by a machine has been a major challenge for many years.With Automatic Speech Recognition (ASR) being decades old and considering the advancement of the technology, where it is not at the point where machines understand all speech, it is used on a regular basis in many applications and services. Hence, to advance research it is important to identify significant research directions, specifically to those that have not been pursued or funded in the past. The performance of such ASR systems, traditionally build upon an Hidden Markov Model (HMM), has improved due tothe application of Deep Neural Networks (DNNs). Despite this progress, building an ASR system remained a challenging task requiring multiple resources and training stages. The idea of using DNNs for Automatic Speech Recognition has gone further from being a single component in a pipeline to building a system mainly based on such a network.This paper provides a literature survey on state of the art researches on two major models, namely Deep Neural Network - Hidden Markov Model (DNN-HMM) and Recurrent Neural Networks trained with Connectionist Temporal Classification (RNN-CTC). It also provides the differences between these two models at the architectural level.

Sign in / Sign up

Export Citation Format

Share Document