Emotion Recognition from Speech with Acoustic, Non-Linear and Wavelet-based Features

132 Pages·2016·4.94 MB·English

Emotion Recognition from Speech with
Acoustic, Non-Linear and Wavelet-based
Features Extracted in Different Acoustic
Conditions
Juan Camilo Vásquez Correa
Department of Electronic and Telecommunication Engineering
Research group on Embedded Systems and Computational Intelligence
SISTEMIC
Master in Telecommunications Engineering
University of Antioquia UdeA February 2016

Emotion Recognition from Speech with
Acoustic, Non-Linear and Wavelet-based
Features Extracted in Different Acoustic
Conditions
Juan Camilo Vásquez Correa
Document in partial fulfillment of the requirements for the degree of:
Master of Science
Director:
Ph.D. Jesús Francisco Vargas Bonilla
University of Antioquia
Department of Electronic and Telecommunication Engineering
Research group on Embedded Systems and Computational Intelligence
SISTEMIC
Medellín, Colombia
February 2016

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Acknowledgements
TherearemanypeopletoIwouldliketothank,whomadepossiblethedevelopmentofthis
work.
First of all I would like to thank to Prof. Jesús Francisco Vargas Bonilla, director of
this study, for his invaluable support, and dedication throughout the development of this
work. His guidance allowed me to grow up in a personal and professional way during the
development of this study. Also to Prof. Juan Rafael Orozco Arroyave for allowing me
discovertheworldofdigitalsignalprocessingandpatternrecognition,andforthesupportin
thedevelopmentofthisstudy. IwouldliketothankalsototheProf. Dr. Ing. ElmarNöthfor
theopportunitytoshareandlearnasamemberofteaminthePatternRecognitionLab. of
theFriedrichAlexanderUniversitätErlangen-Nürnberg.
TotheteamofdigitalsignalprocessingandpatternrecognitionfromtheUniversityof
Antioquia: Tatiana Villa, Elkyn Belalcazar, and Tomas Arias, for the academic and personal
support. Also,totheundergraduatestudentswhoprovidesupportforthedevelopmentofthis
study.
Tomyparents,mysisterandtherestofmyfamilyfortheirinvaluablesupportduringall
theseyears,toallmyfriendsfortheirunconditionalpersonalsupport,andforallowingmeto
sharewiththembothinthegoodandbadtimes.
Tothefoundersofthisstudy: TheInternationalSpeechandCommunicationAssociation
(ISCA) for award me with one of the ISCA student grants to attend the INTERSPEECH
2015inDresden. TheColombianAdministrativeDepartmentofScience,Technologyand
Innovation (COLCIENCIAS) for support this study through the young researchers and
innovators program, in 2014. And the COLCIENCIAS project # 111556933858, titled
"Analysisofthediscriminantcapacityofphonation,articulationandprosodyfeaturesfrom
patients with Parkinson’s disease on preclinic and advanced stages for the development of
computeraidedtoolsforsupportingthediagnosisandmonitoringofthepatients".

Abstract
In the last years, there has a great progress in automatic speech recognition. The chal-
lenge now it is not only recognize the semantic content in the speech but also the called
"paralinguistic" aspects of the speech, including the emotions, and the personality of the
speaker.
Thisresearchworkaimsinthedevelopmentofamethodologyfortheautomaticemotion
recognitionfromspeechsignalsinnon-controllednoiseconditions. Forthatpurpose,different
sets of acoustic, non-linear, and wavelet based features are used to characterize emotions
indifferent databasescreated forsuch purpose. The acousticanalysis considersa standard
feature set developed for emotion recognition from speech called OpenEAR, and a set
of spectral and noise derived measures. The non-linear analysis is based on non-linear
dynamic measures and include the correlation dimension, the largest Lyapunov exponent,
theHurstexponent, andtheLempelZivcomplexity. Alsoitisproposedasetofmeasures
derivedfromparametricnon-stationaryanalysisusingtimedependentARMAmodels. The
wavelet based measures consider features derived from the wavelet packet transform, and
differentwavelettime-frequencyrepresentationssuchasthebionicwavelettransform,and
thesynchro-squeezedwavelettransform.
Differentnon-controllednoiseconditionsaretestedconsideringfourdifferentscenarios:
(1) the original recordings, (2) the signals degraded by two additive noisy environments:
street and a cafeteria babble, (3) the re-captured signals in two natural noisy environments as
streetandoffice,and(4)therecordingscompressedbysevendifferentcodecsusedforthe
transmissionthroughmobile,VoIP,andwebbasedtelephonechannels. Alsotwodifferent
speech enhancement algorithms are tested to evaluate if they are suitable to improve the
results in the classification of emotions in noisy speech signals. A classification scheme
basedonthecombinationofGaussianmixturemodelsandSupportvectormachinesisused
fortheanalysis.