Neuware -Speech recognition has been an integral part of human life acting as one of the five senses of human body, because of which application developed on the basis of speech recognition has high degree of acceptance. The analysis of the different steps involved in isolated word recognition using Mel Frequency cepstral coefficients (MFCC), Vector quantization (VQ) and Hidden Markov Model (HMM) is seen here. The simple and efficient approach is used here which can be utilised in embedded systems. After analysing the steps above we realised the process using small programs using MATLAB which is able to do small number of isolated word recognition.The work done here develops a speaker independent isolated word recognizer from the acoustic signals based on a discrete observation Hidden Markov Model (HMM). The study implements the HMM based isolated word recognizer in three steps- Speech Segmentation,Feature extraction and Feature Matching.Books on Demand GmbH, �berseering 33, 22297 Hamburg 80 pp. Englisch.

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T�tulo: Isolated word recognition using MFCC-LPC-VQ and Hidden Markov Model
Editor: LAP LAMBERT Academic Publishing Aug 2016
Fecha de publicaci�n: 2016
Idioma: Ingl�s
ISBN 10: 3659942839
ISBN 13: 9783659942839
Encuadernaci�n: Taschenbuch
Estado: Neu

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Sinopsis

Speech recognition has been an integral part of human life acting as one of the five senses of human body, because of which application developed on the basis of speech recognition has high degree of acceptance. The analysis of the different steps involved in isolated word recognition using Mel Frequency cepstral coefficients (MFCC), Vector quantization (VQ) and Hidden Markov Model (HMM) is seen here. The simple and efficient approach is used here which can be utilised in embedded systems. After analysing the steps above we realised the process using small programs using MATLAB which is able to do small number of isolated word recognition.The work done here develops a speaker independent isolated word recognizer from the acoustic signals based on a discrete observation Hidden Markov Model (HMM). The study implements the HMM based isolated word recognizer in three steps- Speech Segmentation,Feature extraction and Feature Matching.

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Prof. Mahesh K. Patil completed M.E.(Electronics Engg.) from Shivaji University India. He is currently working as Assistant Professor in DKTES's TEI Ichalkarnji,Maharashtra, India. His academic experience is 6 years. His area of specialization is speech processing, Electromagnetic and Microwave engineering.

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