Speech Recognition by Machine, A Review

By M. Anusuya, S. Katti. arXiv.org, 2010

408 citations recorded by the source corpus.

This paper presents a brief survey on Automatic Speech Recognition and discusses the major themes and advances made in the past 60 years of research, so as to provide a technological perspective and an appreciation of the fundamental progress that has been accomplished in this important area of speech communication. After years of research and development the accuracy of automatic speech recognition remains one of the important research challenges (e.g., variations of the context, speakers, and environment).The design of Speech Recognition system requires careful attentions to the following issues: Definition of various types of speech classes, speech representation, feature extraction techniques, speech classifiers, database and performance evaluation. The problems that are existing in ASR and the various techniques to solve these problems constructed by various research workers have been presented in a chronological order. Hence authors hope that this work shall be a contribution in the area of speech recognition. The objective of this review paper is to summarize and compare some of the well known methods used in various stages of speech recognition system and identify research topic and applications which are at the forefront of this exciting and challenging field.

Subfields: Artificial Intelligence, Signal Processing

View Paper: https://www.semanticscholar.org/paper/5cc6ba30820e7c3b36d314f4deee990dc5655afb

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Signal Processing

Citations in the corpus

Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition (2019, background): “variability, speech domain, noise recognition, and vocabulary volume or size needed or used in speech [102-105].”
Speech Recognition Using Deep Neural Networks: A Systematic Review (2019, background): “past five decades right from the introduction of artificial intelligence [4], [5].”
A Review on Automatic Speech Recognition Architecture and Approaches (2016, background): “The last step in this approach attempts to determine a valid word (or string of words) from the phonetic label sequences produced by the segmentation to labeling [7-8].”
Applying Neural-Network-Based Machine Learning to Additive Manufacturing: Current Applications, Challenges, and Future Perspectives (2019, background): “For example, NNs are the main stimulating force in these areas: computer vision [13], voice recognition [14], natural language processing [15], and autonomous driving [16].”
Interpreting and Explaining Deep Neural Networks for Classification of Audio Signals (2018, background): “Like the visual domain, deep neural networks have fostered progress in audio processing [14, 15, 16, 17], particularly in automatic speech recognition (ASR) [18, 19].”
The Conversation: Deep Audio-Visual Speech Enhancement (2018, background): “Noisy magnitude Spectrogram 1D Residual block, K=5, 1536 1D Residual block, K=5, 1536 1D Conv Bl ck , C=1536 S=1 1D Residual block, K=5, 1536 1D Residual block, K=5, 1536 1D Conv Bl ck , C=1536 S=[1,2,1,2,1] x10 x5 Linear + Concatenation over channels”