The document describes a real-time voice actuation system that uses a source-filter model of speech and support vector machine classification to recognize vocal commands with high accuracy in real time. The system uses a TMS320C5515 DSP chip to handle vocal input and feature extraction via autocorrelation, transmitting data over Bluetooth to a Raspberry Pi for classification using LIBSVM and actuation via GPIO pins. The finished system demonstrates functional voice recognition of the commands "one", "two", "three", and "four".

1. Real Time Voice Actuation System
Pragya Agrawal, Dominic Calabrese, David Martel, Nathan Sawicki
Project Description
Thegoalofourprojectistodesignandbuildareal-timespeechrecognitionsystem.ThisprojectpresentsmanyhardwareandsoftwarechallengesforimplementationinanembeddedenvironmentandisaperfectprojectforEECS452.Usingasourcefiltermodelofspeechandasupportvectormachineclassifieronaprerecordedlibraryofvocalcommands,ourteamwasabletorecognizethecommands“one” “two”“three”and“four”withhighaccuracy.Thefinishedsystemexecutesinreal-timeandhasGPIO-basedactuationtodemonstratefunctionalvoicerecognition.
Hardware
TMS320C5515 eZdsp™ USB Stick Development Tool[1]
VocalinputandfeatureextractionishandledonthisfixedpointDSPchip
Highspeedautocorrelationfunctionidealforfeatureextraction
Raspberry Pi Model B+[3]
700MHzprocessor, BroadComSoCwithfloatingpointunit
40pinGPIO
Idealforrunningclassificationalgorithminrealtime
SparkfunBluetooth Modem –Bluesmirf[2]
PairstheC5515andtheRaspberryPi
PairswithotherBluesmirfmoduleswithrelativeease
115200baudratecapableofreal-timetransmissionandreceiving
Raspberry Pi : Classification & Actuation
C5515 : Detection & Feature Extraction
Implementsthethresholddemonstratedinthefigureontherighttodetectcommands
DSPlibraryprovidessimplefunctionsusefulforreal- timeDSPonC5515
Computesthe16-bitautocorrelationforspeechcommandsandtransmitsthisdataoverbluetoothUART
Support Vector Machine Learning
SupportVectorMachine(SVM)isasupervisedlearningalgorithmusedforanalyzingdataforbinaryclassificationandregressionanalysis.
WeutilizeMulti-classSupportVectorMachine,anextensionofSVMforrealtimeclassificationofspokenwordsintooneofthefourclasses:“ONE”,“TWO”, ”THREE”,”FOUR”
Ouralgorithmusesone-against-onemethodtoconstruct(k*(k-1)/2)classifiers(k=numberofclasses),oneSVMforeachpairofclasses.EachSVMistrainedondatafromtwoclasses,todistinguishthesamplesofoneclassfromanother.Classificationofanunknownpatternisdoneaccordingtomaximumvoting,whereeachSVMvotesforoneclass.
LIBSVMtool,anintegratedsoftwareformulti-classsupportvectorclassificationisused.Itemploysradialbasiskernelfunctionforclassification.
Source-Filter Model of Speech
Wordcharacterizationshouldbeindependentofvolume,pitch,anddurationoftheword
Simplifyspeechproductionmodeltobeing:
1.
Source-vibrationofvocalchords
2.
Filter–vocaltract(i.e.positioningoftongue,mouth,etc.)
Filtermostaffectssoundofwordpositionvocaltractdifferentlytosaydifferentwords
Accuratelymodelingthefilterprovidesabasisforwordrecognition[4]
Broad sweeps of spectrum (formants) result from the filter configuration. Rapidly varying peaks come from source resonances
All-Pole Filter Coefficients
Firstnfiltercoefficientscanberoughlycalculatedusingthefirstntimeshiftsoftheautocorrelationofasignal
Autocorrelationiscomputedbyconvolvingasignalwithtimeshiftedversionofitself
Levinson-DurbinrecursionalgorithmallowsforquickcomputationifthematrixisToeplitzSymmetric
Wanttocapturespectralenvelope,sowant~10filtercoefficients[5]
Too many coefficients leads to over-fitting of curve
References
[1]http://www.spectrumdigital.com/product_info.php?cPath=31&products_id=238
[2]https://www.sparkfun.com/products/12577
[3]http://www.adafruit.com/product/1914
[4]Dutoit,T.,Moreau,N.,Kroon,P.,Howisspeechprocessedinacellphoneconversation?,2009
[5]Rabiner,L.,Schafer,R.,IntroductiontoDigitalSpeechProcessing,2007
Bluesmirf : Bluetooth Communication
Bluetoothimplementationwasagoalfortheteam.Usingpre-configuredBluesmirfdevices,thec5515isabletotransmitUARTdatatotheRaspberryPi.
Transmit‘$$$’overUARTtosendBluesmirfintocommandmode
ProgramtheBluetoothaddressofanotherBluesmirfmoduleintomemory.Transmita‘C’commandtopairthedevices
DevelopedpreliminaryalgorithmusingMATLABanddesktopcomputerstomimicfunctionofC5515andRaspberryPi
UsedMATLABCoderToolboxtoconvertcorealgorithmintoCcode
ImplementedwrapperaroundalgorithmthathandlesUARTcommunication,GPIOtogglingandOpenVGlibraryimagegenerationanddataplotting
ImplementedLibSVMforclassification
EECS 452, Digital Signal Processing Design Lab, Fall 2014