Speech Emotion Recognition Using Fourier Parameters

Wang, Kunxia; An, Ning; Li, Bing Nan; Zhang, Yanyong; Li, Lian

doi:10.1109/taffc.2015.2392101

Cited by 340 publications

(36 citation statements)

References 44 publications

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“…Individuals with schizophrenia display atypical voice patterns, qualitatively described in terms of poverty of speech, increased pauses, distinctive tone and intensity of voice (Alpert et al, 2000;Andreasen et al, 1985;Cohen et al, 2016Cohen et al, , 2012bGalynker et al, 2000;Hoekert et al, 2007;Trémeau et al, 2005). Voice atypicalities have been reported since the first definitions of the disorder (Bleuler, 1911;Kraepelin, 1919), are used in the clinical assessment process, and assume an even stronger relevance in the light of growing findings associating voice patterns to cognitive function, emotional states, and social engagement Cohen and Hong, 2011;Faurholt-Jepsen et al, 2016;Nevler et al, 2017;Pisanski et al, 2016;Trigeorgis et al, 2016;Tsanas et al, 2011;Wang et al, 2015;Williams et al, 2014;Yin et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Voice Patterns in Schizophrenia: A systematic Review and Bayesian Meta-Analysis

Parola

Simonsen

Bliksted

et al. 2019

Preprint

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Voice atypicalities have been a characteristic feature of schizophrenia since its first definitions. They are often associated with core negative symptoms such as flat affect and alogia, and with the social impairments seen in the disorder. This suggests that voice atypicalities may represent a marker of clinical features and social functioning in schizophrenia. We systematically reviewed and meta-analyzed the evidence for distinctive acoustic patterns in schizophrenia, as well as their relation to clinical features. We identified 46 articles, including 55 studies with a total of 1254 patients with schizophrenia and 699 healthy controls. Summary effect sizes (Hedges'g and Pearson's r) estimates were calculated using multilevel Bayesian modeling. We identified weak atypicalities in pitch variability (g = -0.55) related to flat affect, and stronger atypicalities in proportion of spoken time, speech rate, and pauses (g's between -0.75 and -1.89) related to alogia and flat affect. However, the effects were mostly modest (with the important exception of pause duration) compared to perceptual and clinical judgments, and characterized by large heterogeneity between studies.Moderator analyses revealed that tasks with a more demanding cognitive and social component showed larger effects both in contrasting patients and controls and in assessing symptomatology. In conclusion, studies of acoustic patterns are a promising but, yet unsystematic avenue for establishing markers of schizophrenia. We outline recommendations towards more cumulative, open, and theory-driven research.

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Section: Introductionmentioning

confidence: 99%

Voice Patterns in Schizophrenia: A systematic Review and Bayesian Meta-Analysis

Parola

Simonsen

Bliksted

et al. 2019

Preprint

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“…In their method, both domain divergence and emotion discrimination were considered to learn emotion-discriminative and domain-invariant features using emotion label and domain label constraints. Wang et al [44] extracted MFCC, Fourier parameters, fundamental frequency, energy and ZCR from three different databases of Emo-DB, Chinese elderly emotion database (EESDB) and CASIA for recognizing speech emotion. Muthusamy et al [45] extracted a total of 120 wavelet packet energy and entropy features from speech signals and glottal waveforms from Emo-DB, SAVEE and Sahand emotional speech (SES) databases for speech emotion recognition.…”

Section: Related Studiesmentioning

confidence: 99%

“…MFCC1 is the set of MFCC features inspired by the applications of MFCC features [24,40,42]. MFCC2 is the set of features based on MFCC, energy, ZCR and fundamental frequency as inspired by the fusion of MFCC with other acoustic features [11,25,26,44,46]. The HAF is the proposed set of hybrid acoustic features of prosodic and spectral carefully selected based on the interesting results in the literature [11,20,[24][25][26]29,30,39,40,42,44,46,48].…”

Section: Feature Extractionmentioning

confidence: 99%

Ensemble Learning of Hybrid Acoustic Features for Speech Emotion Recognition

Zvarevashe

Olugbara

2020

Algorithms

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Automatic recognition of emotion is important for facilitating seamless interactivity between a human being and intelligent robot towards the full realization of a smart society. The methods of signal processing and machine learning are widely applied to recognize human emotions based on features extracted from facial images, video files or speech signals. However, these features were not able to recognize the fear emotion with the same level of precision as other emotions. The authors propose the agglutination of prosodic and spectral features from a group of carefully selected features to realize hybrid acoustic features for improving the task of emotion recognition. Experiments were performed to test the effectiveness of the proposed features extracted from speech files of two public databases and used to train five popular ensemble learning algorithms. Results show that random decision forest ensemble learning of the proposed hybrid acoustic features is highly effective for speech emotion recognition.

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“…In [34] it is proposed that Fourier parameter features are effective in identifying the different emotions in speech with a focus in developing speaker independent emotion recognition. Salient features from emotional speech signals are extracted and validated and hence proved that the FP features are effective in characterizing and recognizing emotions in speech signals.…”

Section: Speech Signalsmentioning

confidence: 99%

A Survey on Human Emotion Recognition Approaches, Databases and Applications

Vinola¹,

Vimaladevi

2015

ELCVIA

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This paper presents the various emotion classification and recognition systems which implement methods aiming at improving Human Machine Interaction. The modalities and approaches used for affect detection vary and contribute to accuracy and efficacy in detecting emotions of human beings. This paper discovers them in a comparison and descriptive manner. Various applications that use the methodologies in different contexts to address the challenges in real time are discussed. This survey also describes the databases that can be used as standard data sets in the process of emotion identification. Thus an integrated discussion of methods, databases used and applications pertaining to the emerging field of Affective Computing (AC) is done and surveyed. With the advances in the field of emotion recognition, increasing number of investigations have been done and they have found a significant place in different applications involving human computer communication. The primary objective of an emotion recognition system is to interpret the input signals from different modalities and use them to convey information about the interpreted emotion. For example, the doctor may act based on patient's emotions recognized.The nonverbal communication is an important consideration by all these systems. The significant facet of such systems is to study the mechanism behind the non-verbal communication by human beings to be taught in the computer so that the applications can interpret and connect with user's emotions.Previously, publications on surveys have been published in this area where Zeng et al. surveyed the multimodal recognition methods including audio and visual input signals with a focus on spontaneous

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Speech Emotion Recognition Using Fourier Parameters

Cited by 340 publications

References 44 publications

Voice Patterns in Schizophrenia: A systematic Review and Bayesian Meta-Analysis

Voice Patterns in Schizophrenia: A systematic Review and Bayesian Meta-Analysis

Ensemble Learning of Hybrid Acoustic Features for Speech Emotion Recognition

A Survey on Human Emotion Recognition Approaches, Databases and Applications

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