First Progresses in Evaluation of Resonance in Staff Selection through Speech Emotion Recognition

Speech Emotion Recognition (SER) is a hot research topic in the field of Human Computer Interaction. In this paper a SER system is developed with the aim of providing a classification of the "state of interest" of a human subject involved in a job interview. Classification of emotions is performed by analyzing the speech produced during the interview. The presented methods and results show just preliminary conclusions, as the work is part of a larger project including also analysis, investigation and classification of facial expressions and body gestures during human interaction. At the current state of the work, investigation is carried out by using software tools already available for free on the web; furthermore, the features extracted from the audio tracks are analyzed by studying their sensitivity to an audio compression stage. The Berlin Database of Emotional Speech (EmoDB) is exploited to provide the preliminary results.

[1]  K. Scherer,et al.  Vocal cues in emotion encoding and decoding , 1991 .

[2]  Robert Tibshirani,et al.  Classification by Pairwise Coupling , 1997, NIPS.

[3]  Vitoantonio Bevilacqua,et al.  Comparison of data-merging methods with SVM attribute selection and classification in breast cancer gene expression , 2011, BMC Bioinformatics.

[4]  Jason Weston,et al.  Gene Selection for Cancer Classification using Support Vector Machines , 2002, Machine Learning.

[5]  Sergios Theodoridis,et al.  Pattern Recognition, Fourth Edition , 2008 .

[6]  Jonathan Harrington,et al.  The Acoustic Theory of Speech Production , 1999 .

[7]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[9]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[10]  Frank Dellaert,et al.  Recognizing emotion in speech , 1996, Proceeding of Fourth International Conference on Spoken Language Processing. ICSLP '96.

[11]  Gunnar Fant,et al.  Acoustic Theory Of Speech Production , 1960 .

[12]  John C. Platt,et al.  Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .

[13]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[14]  Say Wei Foo,et al.  Speech emotion recognition using hidden Markov models , 2003, Speech Commun..

[15]  Vitoantonio Bevilacqua.  Three-dimensional virtual colonoscopy for automatic polyps detection by artificial neural network approach: New tests on an enlarged cohort of polyps , 2013, Neurocomputing.

[16]  Paul Boersma,et al.  Praat, a system for doing phonetics by computer , 2002 .

[17]  Vitoantonio Bevilacqua,et al.  3D Nose Feature Identification and Localization through Self-Organizing Map and Graph Matching , 2010, J. Circuits Syst. Comput..

[18]  Vitoantonio Bevilacqua,et al.  A face recognition system based on Pseudo 2D HMM applied to neural network coefficients , 2008, Soft Comput..

[19]  J. Mesirov,et al.  Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.

[20]  Roddy Cowie,et al.  Automatic recognition of emotion from voice: a rough benchmark , 2000 .

[21]  Björn W. Schuller,et al.  OpenEAR — Introducing the munich open-source emotion and affect recognition toolkit , 2009, 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops.

[22]  K. Scherer,et al.  Vocal expression of emotion. , 2003 .

[23]  Constantine Kotropoulos,et al.  Emotional speech recognition: Resources, features, and methods , 2006, Speech Commun..

[24]  P. Boersma.  ACCURATE SHORT-TERM ANALYSIS OF THE FUNDAMENTAL FREQUENCY AND THE HARMONICS-TO-NOISE RATIO OF A SAMPLED SOUND , 1993 .

[25]  I. Jolliffe.  Principal Component Analysis , 2002 .

[26]  John H. L. Hansen,et al.  A comparative study of traditional and newly proposed features for recognition of speech under stress , 2000, IEEE Trans. Speech Audio Process..

[27]  A. Enis Çetin,et al.  Teager energy based feature parameters for speech recognition in car noise , 1999, IEEE Signal Processing Letters.

[28]  Günther Palm,et al.  Emotion Recognition from Speech: Stress Experiment , 2008, LREC.

[29]  Astrid Paeschke,et al.  A database of German emotional speech , 2005, INTERSPEECH.

[30]  Ioannis Pitas,et al.  Automatic emotional speech classification , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.