How do you say ‘hello’? Personality impressions from brief novel voices

On hearing a novel voice, listeners readily form personality impressions of that speaker. Accurate or not, these impressions are known to affect subsequent interactions; yet the underlying psychological and acoustical bases remain poorly understood. Furthermore, hitherto studies have focussed on extended speech as opposed to analysing the instantaneous impressions we obtain from first experience. In this paper, through a mass online rating experiment, 320 participants rated 64 sub-second vocal utterances of the word ‘hello’ on one of 10 personality traits. We show that: (1) personality judgements of brief utterances from unfamiliar speakers are consistent across listeners; (2) a two-dimensional ‘social voice space’ with axes mapping Valence (Trust, Likeability) and Dominance, each driven by differing combinations of vocal acoustics, adequately summarises ratings in both male and female voices; and (3) a positive combination of Valence and Dominance results in increased perceived male vocal Attractiveness, whereas perceived female vocal Attractiveness is largely controlled by increasing Valence. Results are discussed in relation to the rapid evaluation of personality and, in turn, the intent of others, as being driven by survival mechanisms via approach or avoidance behaviours. These findings provide empirical bases for predicting personality impressions from acoustical analyses of short utterances and for generating desired personality impressions in artificial voices

Speaker-independent emotion recognition exploiting a psychologically-inspired binary cascade classification schema

In this paper, a psychologically-inspired binary cascade classification schema is proposed for speech emotion recognition. Performance is enhanced because commonly confused pairs of emotions are distinguishable from one another. Extracted features are related to statistics of pitch, formants, and energy contours, as well as spectrum, cepstrum, perceptual and temporal features, autocorrelation, MPEG-7 descriptors, Fujisakis model parameters, voice quality, jitter, and shimmer. Selected features are fed as input to K nearest neighborhood classifier and to support vector machines. Two kernels are tested for the latter: Linear and Gaussian radial basis function. The recently proposed speaker-independent experimental protocol is tested on the Berlin emotional speech database for each gender separately. The best emotion recognition accuracy, achieved by support vector machines with linear kernel, equals 87.7%, outperforming state-of-the-art approaches. Statistical analysis is first carried out with respect to the classifiers error rates and then to evaluate the information expressed by the classifiers confusion matrices. © Springer Science+Business Media, LLC 2011

An investigation into vocal expressions of emotions: the roles of valence, culture, and acoustic factors.

This PhD is an investigation of vocal expressions of emotions, mainly focusing on non-verbal sounds such as laughter, cries and sighs. The research examines the roles of categorical and dimensional factors, the contributions of a number of acoustic cues, and the influence of culture. A series of studies established that naive listeners can reliably identify non-verbal vocalisations of positive and negative emotions in forced-choice and rating tasks. Some evidence for underlying dimensions of arousal and valence is found, although each emotion had a discrete expression. The role of acoustic characteristics of the sounds is investigated experimentally and analytically. This work shows that the cues used to identify different emotions vary, although pitch and pitch variation play a central role. The cues used to identify emotions in non-verbal vocalisations differ from the cues used when comprehending speech. An additional set of studies using stimuli consisting of emotional speech demonstrates that these sounds can also be reliably identified, and rely on similar acoustic cues. A series of studies with a pre-literate Namibian tribe shows that non-verbal vocalisations can be recognized across cultures. An fMRI study carried out to investigate the neural processing of non-verbal vocalisations of emotions is presented. The results show activation in pre-motor regions arising from passive listening to non-verbal emotional vocalisations, suggesting neural auditory-motor interactions in the perception of these sounds. In sum, this thesis demonstrates that non-verbal vocalisations of emotions are reliably identifiable tokens of information that belong to discrete categories. These vocalisations are recognisable across vastly different cultures and thus seem to, like facial expressions of emotions, comprise human universals. Listeners rely mainly on pitch and pitch variation to identify emotions in non verbal vocalisations, which differs with the cues used to comprehend speech. When listening to others' emotional vocalisations, a neural system of preparatory motor activation is engaged

The effect of foreground and background of soundscape sequence on emotion in urban open spaces

This paper discusses the influence of the soundscape sequence of different urban open spaces on emotion. Thirty participants with normal hearing were selected to listen to forty-two different acoustic sequences and report their emotional changes during the process. The data were analysed in four stages, and the results are as follows: First, emotional response highly correlates with background type. Only when the foreground is negative does it relate to the foreground type. Second, the positive foreground in the early part of a sequence, or the neutral (or negative) foreground in the later part of a sequence, induces a better emotional experience. Third, in an acoustic sequence, emotion changes along with a change in the foreground. The appearance of the foreground triggers emotional fluctuations, and the end of the foreground is followed by emotional recovery. Finally, combining foregrounds can aid in regulating negative emotions. This effect is related to the position of the positive foreground and background type. We offer suggestions on the design of urban soundscape from the perspective of emotion based on the findings