Use of line spectral frequencies for emotion recognition from speech

We propose the use of the line spectral frequency (LSF) features for emotion recognition from speech, which have not been been previously employed for emotion recognition to the best of our knowledge. Spectral features such as mel-scaled cepstral coefficients have already been successfully used for the parameterization of speech signals for emotion recognition. The LSF features also offer a spectral representation for speech, moreover they carry intrinsic information on the formant structure as well, which are related to the emotional state of the speaker. We use the Gaussian mixture model (GMM) classifier architecture, that captures the static color of the spectral features. Experimental studies performed over the Berlin Emotional Speech Database and the FAU Aibo Emotion Corpus demonstrate that decision fusion configurations with LSF features bring a consistent improvement over the MFCC based emotion classification rates.TUBİTAK ; Bahçeşehir University Research Fun

Use of line spectral frequencies for emotion recognition from speech

We propose the use of the line spectral frequency (LSF) features for emotion recognition from speech, which have not been been previously employed for emotion recognition to the best of our knowledge. Spectral features such as mel-scaled cepstral coefficients have already been successfully used for the parameterization of speech signals for emotion recognition. The LSF features also offer a spectral representation for speech, moreover they carry intrinsic information on the formant structure as well, which are related to the emotional state of the speaker. We use the Gaussian mixture model (GMM) classifier architecture, that captures the static color of the spectral features. Experimental studies performed over the Berlin Emotional Speech Database and the FAU Aibo Emotion Corpus demonstrate that decision fusion configurations with LSF features bring a consistent improvement over the MFCC based emotion classification rates.TUBİTAK ; Bahçeşehir University Research Fun

Determination of Formant Features in Czech and Slovak for GMM Emotional Speech Classifier

The paper is aimed at determination of formant features (FF) which describe vocal tract characteristics. It comprises analysis of the first three formant positions together with their bandwidths and the formant tilts. Subsequently, the statistical evaluation and comparison of the FF was performed. This experiment was realized with the speech material in the form of sentences of male and female speakers expressing four emotional states (joy, sadness, anger, and a neutral state) in Czech and Slovak languages. The statistical distribution of the analyzed formant frequencies and formant tilts shows good differentiation between neutral and emotional styles for both voices. Contrary to it, the values of the formant 3-dB bandwidths have no correlation with the type of the speaking style or the type of the voice. These spectral parameters together with the values of the other speech characteristics were used in the feature vector for Gaussian mixture models (GMM) emotional speech style classifier that is currently developed. The overall mean classification error rate achieves about 18 %, and the best obtained error rate is 5 % for the sadness style of the female voice. These values are acceptable in this first stage of development of the GMM classifier that should be used for evaluation of the synthetic speech quality after applied voice conversion and emotional speech style transformation

BaNa: a noise resilient fundamental frequency detection algorithm for speech and music

Fundamental frequency (F0) is one of the essential features in many acoustic related applications. Although numerous F0 detection algorithms have been developed, the detection accuracy in noisy environments still needs improvement. We present a hybrid noise resilient F0 detection algorithm named BaNa that combines the approaches of harmonic ratios and Cepstrum analysis. A Viterbi algorithm with a cost function is used to identify the F0 value among several F0 candidates. Speech and music databases with eight different types of additive noise are used to evaluate the performance of the BaNa algorithm and several classic and state-of-the-art F0 detection algorithms. Results show that for almost all types of noise and signal-to-noise ratio (SNR) values investigated, BaNa achieves the lowest Gross Pitch Error (GPE) rate among all the algorithms. Moreover, for the 0 dB SNR scenarios, the BaNa algorithm is shown to achieve 20% to 35% GPE rate for speech and 12% to 39% GPE rate for music. We also describe implementation issues that must be addressed to run the BaNa algorithm as a real-time application on a smartphone platform.Peer ReviewedPostprint (author's final draft

An Analysis of Rhythmic Staccato-Vocalization Based on Frequency Demodulation for Laughter Detection in Conversational Meetings

Human laugh is able to convey various kinds of meanings in human communications. There exists various kinds of human laugh signal, for example: vocalized laugh and non vocalized laugh. Following the theories of psychology, among all the vocalized laugh type, rhythmic staccato-vocalization significantly evokes the positive responses in the interactions. In this paper we attempt to exploit this observation to detect human laugh occurrences, i.e., the laughter, in multiparty conversations from the AMI meeting corpus. First, we separate the high energy frames from speech, leaving out the low energy frames through power spectral density estimation. We borrow the algorithm of rhythm detection from the area of music analysis to use that on the high energy frames. Finally, we detect rhythmic laugh frames, analyzing the candidate rhythmic frames using statistics. This novel approach for detection of `positive' rhythmic human laughter performs better than the standard laughter classification baseline.Comment: 5 pages, 1 figure, conference pape

On the analysis of EEG power, frequency and asymmetry in Parkinson's disease during emotion processing

Objective: While Parkinson’s disease (PD) has traditionally been described as a movement disorder, there is growing evidence of disruption in emotion information processing associated with the disease. The aim of this study was to investigate whether there are specific electroencephalographic (EEG) characteristics that discriminate PD patients and normal controls during emotion information processing. Method: EEG recordings from 14 scalp sites were collected from 20 PD patients and 30 age-matched normal controls. Multimodal (audio-visual) stimuli were presented to evoke specific targeted emotional states such as happiness, sadness, fear, anger, surprise and disgust. Absolute and relative power, frequency and asymmetry measures derived from spectrally analyzed EEGs were subjected to repeated ANOVA measures for group comparisons as well as to discriminate function analysis to examine their utility as classification indices. In addition, subjective ratings were obtained for the used emotional stimuli. Results: Behaviorally, PD patients showed no impairments in emotion recognition as measured by subjective ratings. Compared with normal controls, PD patients evidenced smaller overall relative delta, theta, alpha and beta power, and at bilateral anterior regions smaller absolute theta, alpha, and beta power and higher mean total spectrum frequency across different emotional states. Inter-hemispheric theta, alpha, and beta power asymmetry index differences were noted, with controls exhibiting greater right than left hemisphere activation. Whereas intra-hemispheric alpha power asymmetry reduction was exhibited in patients bilaterally at all regions. Discriminant analysis correctly classified 95.0% of the patients and controls during emotional stimuli. Conclusion: These distributed spectral powers in different frequency bands might provide meaningful information about emotional processing in PD patients