Big Five vs. Prosodic Features as Cues to Detect Abnormality in SSPNET-Personality Corpus

Fayet, Cédric; Delhay, Arnaud; Lolive, Damien; Marteau, Pierre-François

doi:10.21437/interspeech.2017-1194

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…[13] found that predicting EX using classifiers trained only on the male, female, or journalist subgroups resulted in higher accuracies than all of these groups combined. [14] demonstrated features based on psychological information can bring more information to personality perception than audio features only. Despite this progress, SPC's potential has not been fully exploited due to its small size.…”

Section: Personality Perception Using Spcmentioning

confidence: 99%

Transfer Learning for Personality Perception via Speech Emotion Recognition

Li¹,

Bell²,

Lai³

2023

Interspeech 2023

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Holistic perception of affective attributes is an important human perceptual ability. However, this ability is far from being realized in current affective computing, as not all of the attributes are well studied and their interrelationships are poorly understood. In this work, we investigate the relationship between two affective attributes: personality and emotion, from a transfer learning perspective. Specifically, we transfer Transformerbased and wav2vec2-based emotion recognition models to perceive personality from speech across corpora. Compared with previous studies, our results show that transferring emotion recognition is effective for personality perception. Moreoever, this allows for better use and exploration of small personality corpora. We also provide novel findings on the relationship between personality and emotion that will aid future research on holistic affect recognition.

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Section: Personality Perception Using Spcmentioning

confidence: 99%

Transfer Learning for Personality Perception via Speech Emotion Recognition

Li¹,

Bell²,

Lai³

2023

Interspeech 2023

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“…The choice for the different statistical models tested in this paper is driven by speaker identification and emotion recognition techniques: Gaussian Mixture Models (GMM) and Support Vector Machines (SVM). However, direct speech samples can also be considered as outliers among narrative samples and outlier detection techniques [23] such as one-class models, are also considered.…”

Section: Discourse Modelsmentioning

confidence: 99%

Discourse phrases classification: direct vs. narrative audio speech

Tahon¹,

Lolive²

2018

Speech Prosody 2018

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In the field of storytelling, speech synthesis is trying to move from a neutral machine-like to an expressive voice. For parametric and unit-selection systems, building new features or cost functions is necessary to allow a better expressivity control. The present article investigates the classification task between direct and narrative discourse phrases to build a new expressivity score. Different models are trained on different speech units (syllable, word and discourse phrases) from an audiobook with 3 sets of features. Classification experiments are conducted on the Blizzard corpus which features children English audiobooks and contains various characters and emotional states. The experiments show that the fusion of SVM classifiers trained with different prosodic and phonologic feature sets increases the classification rate from 67.4% with 14 prosodic features to 71.8% with the 3 merged sets. Also the addition of a decision threshold achieves promising results for expressive speech synthesis according to the strength of the constraint required on expressivity: 71.8% with 100% of the words, 79.9% with 50% and 82.6% with 25%.

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“…Studies were not limited only to the acoustic and nonverbal features extraction. The lexical [8], [16], knowledge-based features [14], and BFI questionnaire scores [17] were added to this variety of features as well. The effort toward achieving a wider variety of features reached 6,373 statistical feature in [18].…”

Section: Introductionmentioning

confidence: 99%

Automatic Personality Traits Perception Using Asymmetric Auto-Encoder

Zaferani¹,

Teshnehlab²,

Vali³

2021

IEEE Access

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On account of an increase in the human-computer interface applications, the study of automatic personality perception has become more and more prevalent than speech signal processing in recent years. These studies have shown that personality traits derived from psychology theories mainly affect acoustic features. However, some obstacles remain in the automatic personality perception classification, and the most important one is to extract the features related to each personality trait. Previous studies have shown that the personality effect differs from one acoustic feature to the others. Additionally, there are many features one can extract from speech signals. Curse of dimensionality in features also makes the classification difficult. This paper aimed to introduce and examine a novel and efficient automatic feature extraction method to classify the well-known big five personality traits. In this regard, three data augmentation methods for increasing data samples were examined. Afterwards, 6,373 statistical features were extracted from the nonverbal features of the SSPNet Speaker Personality Corpus. Finally, an innovative stacked asymmetric auto-encoder was utilized to extract useful features automatically to improve classification results. Compared with the conventional stacked auto-encoder and convolutional neural network, the proposed method exhibited an average improvement of 12.40%(10.14%) and 14.36%(1.42%) in terms of the unweighted average recall (accuracy), respectively. In comparison with other published works, classification results also revealed a notable average enhancement (11.78%) for unweighted average recall for all five traits and an average improvement of 5.1% for accuracy in two out of five personality traits.

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Big Five vs. Prosodic Features as Cues to Detect Abnormality in SSPNET-Personality Corpus

Cited by 3 publications

References 15 publications

Transfer Learning for Personality Perception via Speech Emotion Recognition

Transfer Learning for Personality Perception via Speech Emotion Recognition

Discourse phrases classification: direct vs. narrative audio speech

Automatic Personality Traits Perception Using Asymmetric Auto-Encoder

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