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Röstigenkänning genom Hidden Markov Model: En implementering av teorin på DSP
Blekinge Institute of Technology, School of Engineering, Department of Signal Processing.
Blekinge Institute of Technology, School of Engineering, Department of Signal Processing.
2006 (English)Independent thesis Advanced level (degree of Master (One Year))Student thesisAlternative title
Speech Recognition using Hidden Markov Model : An implementation of the theory on a DSK – ADSP-BF533 EZ-KIT LITE REV 1.5 (Swedish)
Abstract [en]

This master degree project is how to implement a speech recognition system on a DSK – ADSP-BF533 EZ-KIT LITE REV 1.5 based on the theory of the Hidden Markov Model (HMM). The implementation is based on the theory in the master degree project Speech Recognition using Hidden Markov Model by Mikael Nilsson and Marcus Ejnarsson, MEE-01-27. The work accomplished in the project is by reference to the theory, implementing a MFCC, Mel Frequency Cepstrum Coefficient function, a training function, which creates Hidden Markov Models of specific utterances and a testing function, testing utterances on the models created by the training-function. These functions where first created in MatLab. Then the test-function where implemented on the DSK. An evaluation of the implementation is performed.

Abstract [sv]

Detta examensarbete går ut på att implementera en röstigenkänningssystem på en DSK – ADSP-BF533 EZ-KIT LITE REV 1.5 baserad på teorin om HMM, Hidden Markov Model. Implementeringen är baserad på teorin i examensarbetet Speech Recognition using Hidden Markov Model av Mikael Nilsson och Marcus Ejnarsson, MEE-01-27. Det som gjorts i arbetet är att utifrån teorin implementerat en MFCC, Mel Frequency Cepstrum Coefficient funktion, en träningsfunktion som skapar Hidden Markov Modeller av unika uttalanden av ord och en testfunktion som testar ett uttalat ord mot de olika modellerna som skapades av träningsfunktionen. Dessa funktioner skapades först i MatLab. Sedan implementerades testprogrammet på DSP:n Texas Instruments TMDS320x6711. Sedan utvärderades realtidstillämpningen.

Place, publisher, year, edition, pages
2006. , 79 p.
Keyword [en]
Speech Recognition, Hidden Markov, Signalbehandling, DSP, DSK, BF533, Nick Bardici, Björn Skarin
National Category
Signal Processing Telecommunications Software Engineering
Identifiers
URN: urn:nbn:se:bth-1414Local ID: oai:bth.se:arkivexE63C30F71DDE8E8DC1257142004558B3OAI: oai:DiVA.org:bth-1414DiVA: diva2:828650
Uppsok
Technology
Supervisors
Note
Nick Bardici, nick.bardici@gmail.com Björn Skarin, bjorn.skarin@exallon.sigma.seAvailable from: 2015-05-20 Created: 2006-03-31 Last updated: 2015-06-30Bibliographically approved

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