Spoken Keyword Based Speech Recognizer for Extracting Information (original) (raw)
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The paper presents the design of speech recognition system that uses preprocessing, feature extraction and classification stages. In preprocessing stage a de-noising is done to get the speech data without noise. In feature extraction stage Linear Predictive Coding (LPC), Mel Frequency Cepstral Coefficients (MFCC), and Spectrogram methods are used to extract the features of the word. Neural Networks (NN) was used to classify the spoken words to different patterns so the system can recognize unknown spoken words according to these patterns. The set of spoken words are used in simulation of the system. The comparative results of the system have been provided using above mentioned feature extraction methods.
Speech Recognition System with Different Methods of Feature Extraction
International Journal of Innovative Research in Computer and Communication Engineering, 2018
The paper presents the design of speech recognition system that uses preprocessing, feature extraction and classification stages. In preprocessing stage a de-noising is done to get the speech data without noise. In feature extraction stage Linear Predictive Coding (LPC), Mel Frequency Cepstral Coefficients (MFCC), and Spectrogram methods are used to extract the features of the word. Neural Networks (NN) was used to classify the spoken words to different patterns so the system can recognize unknown spoken words according to these patterns. The set of spoken words are used in simulation of the system. The comparative results of the system have been provided using above mentioned feature extraction methods.
Double ended speech enabled system in Indian travel & tourism industry
2013 IEEE International Conference on Computational Intelligence and Computing Research, 2013
In this research paper we emphasized on development of double ended voice enabled system in order to receive the voice query and convey voiced output message related to travel and tourism domain in Indian language. The voice enable system was developed using multiple components such as automatic speech recognizer (ASR) engine, query classifier and speech synthesis engine. The speech recognition engine plays very crucial role in speech based system which we have evaluated using multiple pattern recognition algorithms namely Hidden Markov Model (HMM), Support Vector Machine (SVM), ontology based feed forward back propagation neural network (OFFBPNN), dynamic time warping (DTW). The performance of SVM AND HMM were seen superior with respect to OFFBPNN, DTW which were measured in terms of word accuracy and word error rate. The output of ASR is fed to k-nearest neighbour (KNN) query classifier and the end result of classifier is finally passed to Odia speech synthesizer to deliver the response in voice mode. We have employed voice transformation technique in speech synthesis system to produce the spoken output in male, female, child and robotic voice. The developed double ended voice enabled system is operational over Odia spoken query and delivered the response in synthesized Odia voice.
AUTOMATIC SPEECH RECOGNITION- A SURVEY
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
Feature Extraction and Classification Techniques for Speech Recognition: A Review
2013
Speech is the most natural form of human communication and speech processing has been one of the most inspiring expanses of signal processing. Speech recognition is the process of automatically recognizing the spoken words of person based on information in speech signal. Automatic Speech Recognition (ASR) system takes a human speech utterance as an input and requites a string of words as output. This paper introduce a brief survey on Automatic Speech Recognition and discuss the major subjects and improvements made in the past 60 years of research, that provides technological outlook and a respect of the fundamental achievement that has been accomplished in this important area of speech communication. Definition of various types of speech classes, feature extraction techniques, speech classifiers and performance evaluation are issues that requires attention in designing of speech recognition system. The objective of this review paper is to summarize some of the well known methods use...
A Review on: Speech Recognition System
This paper presents a brief survey on Speech recognition and discusses major themes and advances. Automatic speech recognition uses the process and related technology for converting speech signals into a sequence of words or other linguistic units by means of an algorithm implemented as a computer program. After years of research and development the accuracy of automatic speech recognition remains one of the important research challenges. Speech understanding systems presently are capable of understanding speech input for vocabularies of thousands of words in operational environments. Speech Recognition offers greater freedom to employ the physically handicapped in several applications like manufacturing processes, medicine and telephone network. 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.
A Comparative Study of Feature Extraction Techniques for Speech Recognition System
The automatic recognition of speech means enabling a natural and easy mode of communication between human and machine. Speech processing has vast applications in voice dialing, telephone communication, call routing, domestic appliances control, Speech to Text conversion, Text to Speech conversion, lip synchronization, automation systems etc. Here we have discussed some mostly used feature extraction techniques like Mel frequency Cepstral Co-efficient (MFCC), Linear Predictive Coding (LPC) Analysis, Dynamic Time Wrapping (DTW), Relative Spectra Processing (RASTA) and Zero Crossings with Peak Amplitudes (ZCPA).Some parameters like RASTA and MFCC considers the nature of speech while it extracts the features, while LPC predicts the future features based on previous features.
Speech Recognition System: A Review
International Journal of Computer Applications, 2015
To be able to control devices by voice has always intrigued mankind. Today after intense research, Speech Recognition System, have made a niche for themselves and can be seen in many walks of life. The accuracy of Speech Recognition Systems remains one of the most important research challenges e.g. noise, speaker variability, language variability, vocabulary size and domain. The design of speech recognition system requires careful attentions to the challenges such as various types of Speech Classes and Speech Representation, Speech Preprocessing stages, Feature Extraction techniques, Database and Performance evaluation. This paper presents the advances made as well as highlights the pressing problems for a speech recognition system. The paper also classifies the system into Front End and Back End for better understanding and representation of speech recognition system in each part.
This paper analyses the audio inconsistency of speakers and its impact on the strength of existing automatic speech recognition and speaker recognition systems. The acoustic and visual features are evaluated by a Support Vector Machine for digit and speaker detection and later by Hidden Markov Model verification. A methodology for speech recognition with speaker recognition based on Hidden Markov Model for security is a requirement of science. Mapping of speech using Artificial Neural networks is obtainable. Fireflies create glowing flash as a sign scheme to correspond with additional fireflies particularly to prey attractions. Cuckoo search algorithm is used as its search space is extensive in nature. Genetic algorithm can shun calculating system slope in traditional gap investigation and determines the optimum interval range of the parameters under acceptable corresponding aim error boundary. In order to obtain to obtain the most efficient and linearly discriminative components, LDA is used.
Automatic Speech Recognition System: A Review
International Journal of Computer Applications, 2016
Speech is the most prominent & primary mode of Communication among human beings. Now-a-days Speech also has potential of being important mode of interaction with computers. This paper gives an overview of Automatic Speech Recognition System, Classification of Speech Recognition System and also includes overview of the steps followed for developing the Speech Recognition System in stages. This paper also helps in choosing the tool and technique along with their relative merits & demerits. A comparative study of different techniques is also included in this paper.