Automatic Speech Recognition System

This paper reviews some of various research carried out over the last decade in the area of Automatic Speech Recognition (ASR) and discusses the major themes and advance made in the last decade of research, in order to show the outlook of technology and an appreciation of the fundamental progress that has been achieved in this weighty area of speech communication. Over period of research and development, the accuracy of automatic speech recognition remains one of the important research challenges such as variation of the context, environmental condition, speaker's variation and poor-quality audio. The design of speech recognition requires careful attention to the following issue: Definition of various types of speech classes, speech representation, techniques, database and performance evaluation. The history, challenges of speech recognition system and various techniques to solve these challenges constructed by various research works have been presented in a chronological order. The objective of this paper is to compare and summarize well know approaches used in various steps of speech recognition system.

It is a well known fact that, speech recognition systems perform well when the system is used in conditions similar to the one used to train the acoustic models. However, mismatches degrade the performance. In adverse environment, it is very difficult to predict the category of noise in advance in case of real world environmental noise and difficult to achieve environmental robustness. After doing rigorous experimental study it is observed that, a unique method is not available that will clean the noisy speech as well as preserve the quality which have been corrupted by real natural environmental (mixed) noise. It is also observed that only back-end techniques are not sufficient to improve the performance of a speech recognition system. It is necessary to implement performance improvement techniques at every step of back-end as well as front-end of the Automatic Speech Recognition (ASR) model. Current recognition systems solve this problem using a technique called adaptation. This study presents an experimental study that aims two points, first is to implement the hybrid method that will take care of clarifying the speech signal as much as possible with all combinations of filters and enhancement techniques. The second point is to develop a method for training all categories of noise that can adapt the acoustic models for a new environment that will help to improve the performance of the speech recognizer under real world environmental mismatched conditions. This experiment confirms that hybrid adaptation methods improve the ASR performance on both levels, (Signal-to-Noise Ratio) SNR improvement as well as word recognition accuracy in real world noisy environment.

VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE, 2019

In order to make fast communication between human and machine, speech recognition system are used. Number of speech recognition systems have been developed by various researchers. For example speech recognition, speaker verification and speaker recognition. The basic stages of speech recognition system are pre-processing, feature extraction and feature selection and classification. Numerous works have been done for improvement of all these stages to get accurate and better results. In this paper the main focus is given to addition of machine learning in speech recognition system. This paper covers architecture of ASR that helps in getting idea about basic stages of speech recognition system. Then focus is given to the use of machine learning in ASR. The work done by various researchers using Support vector machine and artificial neural network is also covered in a section of the paper. Along with this review is presented on work done using SVM, ELM, ANN, Naive Bayes and kNN classifi...

Noise in everyday acoustic environments such as cars, traffic environments, and cafeterias remains one of the main challenges in automatic speech recognition (ASR). As a reserch theme, it has received wide attention in conferences and scientific journals focused on speech technology. This article collection reviews both the classic and novel approaches suggested for noise robust ASR. The articles are literature reviews written for the spring 2009 seminar course on noise robust automatic speech recognition (course code T-61.6060) held at TKK.

Speech recognition is the next big step that the technology needs to take for general users. An Automatic Speech Recognition (ASR) will play a major role in focusing new technology to users. Applications of ASR are speech to text conversion, voice input in aircraft, data entry, voice user interfaces such as voice dialing. Speech recognition involves extracting features from the input signal and classifying them to classes using pattern matching model. This can be done using feature extraction method. This paper involves a general study of automatic speech recognition and various methods to generate an ASR system. General techniques that can be used to implement an ASR includes artificial neural networks, Hidden Markov model, acoustic – phonetic approach

2017

Speech is the most natural form of communication and interaction between humans. In computer system, the text and symbols are the most common form of translation. Speech Recognition is an important application that enables interaction of human being with machines. The various stages in the speech recognition system are pre-processing, segmentation of speech signal, feature extraction of speech and recognition stage. Among many speech recognition systems, continuous speech recognition system is very important and most popular system. This paper presents the basic idea of speech recognition, proposed types of speech recognition techniques, issues in speech recognition, different useful approaches for noise filtering, features extraction of the speech signal with its advantages and disadvantages and various pattern matching approaches for recognizing the speech of the speakers. Now day’s research in speech recognition system is motivated for ASR system with a large vocabulary that supp...

A Study on Automatic Speech Recognition, 2019

Speech is an easy and usable technique of communication between humans, but nowadays humans are not limited to connecting to each other but even to the different machines in our lives. The most important is the computer. So, this communication technique can be used between computers and humans. This interaction is done through interfaces, this area called Human Computer Interaction (HCI). This paper gives an overview of the main definitions of Automatic Speech Recognition (ASR) which is an important domain of artificial intelligence and which should be taken into account during any related research (Type of speech, vocabulary size... etc.). It also gives a summary of important research relevant to speech processing in the few last years, with a general idea of our proposal that could be considered as a contribution in this area of research and by giving a conclusion referring to certain enhancements that could be in the future works.

Language is the most important means of communication and speech is its main medium. In human to machine interface, speech signal is transformed into analog and digital wave form which can be understood by machine. Speech technologies are vastly used and has unlimited uses. These technologies enable machines to respond correctly and reliably to human voices, and provide useful and valuable services .This paper gives an overview of the speech recognition process, its basic model, and its application, approaches and also discuss comparative study of different approaches which are used for speech recognition system. These papers also give an overview of different techniques of speech recognition system to summarize some of the well known methods used in various stages of speech recognition system.

International Journal of Computer Applications, 2016

Speech is the nature's gift to the human being which contributes towards the intelligence and discrimination from rest of the animal kingdom. Taking into consideration technological aspects, speech recognition is the buzzword today, as communication and hands free computing evolving day by day. Speech is a very important mode of the communication and interaction with the digital computer. Speech recognition along with the wide range of applicability in domain of computer science, medical science, psychology, sports, neurology has many challenges while developing. Developing real time speech recognizer may hurdle from adverse environment to anatomy of the human body. It also involves linguistic aspects too. This paper explores various challenges in developing a robust ASR system.

International journal of advance engineering & research development, 2014

Speech interface to computer is the next big step that the technology needs to take for general users. Automatic speech recognition (ASR) will play an important role in taking technology to the people. There are numerous applications of speech recognition such as direct voice input in aircraft, data entry, speech-to-text processing, voice user interfaces such as voice dialing. ASR system can be divided into two different parts, namely feature extraction and feature recognition. This paper provides an overview for Speech recognition where Acoustic modeling techniques, Feature extraction techniques for Speech recognition are briefly discussed.