228 research outputs found
Feature Learning from Spectrograms for Assessment of Personality Traits
Several methods have recently been proposed to analyze speech and
automatically infer the personality of the speaker. These methods often rely on
prosodic and other hand crafted speech processing features extracted with
off-the-shelf toolboxes. To achieve high accuracy, numerous features are
typically extracted using complex and highly parameterized algorithms. In this
paper, a new method based on feature learning and spectrogram analysis is
proposed to simplify the feature extraction process while maintaining a high
level of accuracy. The proposed method learns a dictionary of discriminant
features from patches extracted in the spectrogram representations of training
speech segments. Each speech segment is then encoded using the dictionary, and
the resulting feature set is used to perform classification of personality
traits. Experiments indicate that the proposed method achieves state-of-the-art
results with a significant reduction in complexity when compared to the most
recent reference methods. The number of features, and difficulties linked to
the feature extraction process are greatly reduced as only one type of
descriptors is used, for which the 6 parameters can be tuned automatically. In
contrast, the simplest reference method uses 4 types of descriptors to which 6
functionals are applied, resulting in over 20 parameters to be tuned.Comment: 12 pages, 3 figure
Transfer Learning for Personality Perception via Speech Emotion Recognition
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 Transformer-based 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.Comment: Accepted to INTERSPEECH 202
- …