Emotion in the Speech of Children with Autism Spectrum Conditions: Prosody and Everything Else (original) (raw)

Voice Emotion Games: Language and Emotion in the Voice of Children with Autism Spectrum Condition

Children with Autism Spectrum Conditions (ASC) may experience significant difficulties to recognise and express emotions. The ASC-Inclusion project set up an internet-based digital gaming experience that assists children with ASC to improve their socio-emotional communication skills, combining voice, face, and body gesture analysis, and giving corrective feedback regarding the appropriateness of the child's expressions. The present contribution focuses on the recognition of emotion in speech. For this purpose, a database of prompted phrases was collected in English, Swedish, and Hebrew, inducing nine emotions embedded in short-stories. It contains speech of children with ASC and typically developing children under the same conditions. We evaluate the emotion task over the nine categories, by investigating the discrimination of each emotion against the remaining ones. The results show performances up to 83.8% unweighted average recall.

Automatic Analysis of Typical and Atypical Encoding of Spontaneous Emotion in the Voice of Children

Interspeech 2016, 2016

Children with Autism Spectrum Disorders (ASD) present significant difficulties to understand and express emotions. Systems have thus been proposed to provide objective measurements of acoustic features used by children suffering from ASD to encode emotion in speech. However, only a few studies have exploited such systems to compare different groups of children in their ability to express emotions, and even less have focused on the analysis of spontaneous emotion. In this contribution, we provide insights by extensive evaluations carried out on a new database of spontaneous speech inducing three emotion categories of valence (positive, neutral, and negative). We evaluate the potential of using an automatic recognition system to differentiate groups of children, i.e., pervasive developmental disorders, pervasive developmental disorders not-otherwise specified, specific language impairments, and typically developing, in their abilities to express spontaneous emotion in a common unconstrained task. Results show that all groups of children can be differentiated directly (diagnosis recognition) and indirectly (emotion recognition) by the proposed system.

Hybrid model for speech emotion recognition of normal and autistic children (SERNAC)

Mehran University research journal of engineering and technology, 2024

Since the last decade, autism spectrum disorder (ASD) has been used as a general term to describe a wide range of conditions, including autistic syndrome, Asperger's disorder, and pervasive developmental disability. This problem emerges as a decreased ability to share emotions and a greater difficulty understanding others' feelings, leading to increased social communication difficulties. To assist patients with ASD, we proposed a concept that incorporates speech emotion detection technologies, which are widely used in the field of human-computer interaction (particularly youngsters). An algorithm based on a novel method for classifying normal and autistic children's speech emotions is implemented in this article. The training data set is treated to a new approach after all features have been extracted. The technique discussed in this study is the creation of a hybrid algorithm that serves as a classifier for normal and autistic children's speech emotions. Voice emotion recognition can be identified accurately and with a lower error rate. The data collection includes speech samples from 200 normal and 250 autistic groups in four moods (Angry, Happy, Neutral and Sad). As per research findings, the implemented hybrid algorithm for Normal and Autistic Children Speech Emotions (SERNAC) outperformed the existing classifiers by increasing accuracy.

AutisMitr: Emotion Recognition Assistive Tool for Autistic Children

Open Computer Science

Assistive technology has proven to be one of the most significant inventions to aid people with Autism to improve the quality of their lives. In this study, a real-time emotion recognition system for autistic children has been developed. Emotion recognition is implemented by executing three stages: Face identification, Facial Feature extraction, and feature classification. The objective is to frame a system that includes all three stages of emotion recognition activity that executes expeditiously in real time. Thus, Affectiva SDK is implemented in the application. The propound system detects at most 7 facial emotions: anger, disgust, fear, joy, sadness, contempt, and surprise. The purpose for performing this study is to teach emotions to individuals suffering from autism, as they lack the ability to respond appropriately to others emotions. The proposed application was tested with a group of typical children aged 6–14 years, and positive outcomes were achieved.

Recognition of Emotions for People with Autism: An Approach to Improve Skills

International Journal of Computer Games Technology

Autism spectrum disorder refers to a neurodevelopmental disorders characterized by repetitive behavior patterns, impaired social interaction, and impaired verbal and nonverbal communication. The ability to recognize mental states from facial expressions plays an important role in both social interaction and interpersonal communication. Thus, in recent years, several proposals have been presented, aiming to contribute to the improvement of emotional skills in order to improve social interaction. In this paper, a game is presented to support the development of emotional skills in people with autism spectrum disorder. The software used helps to develop the ability to recognize and express six basic emotions: joy, sadness, anger, disgust, surprise, and fear. Based on the theory of facial action coding systems and digital image processing techniques, it is possible to detect facial expressions and classify them into one of the six basic emotions. Experiments were performed using four pub...

The Perception of Affective Prosody in Children with Autism Spectrum Disorders and Typical Peers

Clinical Archives of Communication Disorders

This study investigated the ability of children with ASD, including the minimally verbal subgroup, to perceive angry, neutral, and happy prosody in low-pass filtered speech when provided with a structured training paradigm. Methods: 13 children with ASD and 21 TD children completed the experimental task and two additional measures (nonverbal cognitive abilities, social responsiveness deficits) for regression analyses. Results: The ASD group recognized prosodic conditions significantly less accurately than the TD group, and took significantly longer times to recognize all sentences compared to the TD group. Angry prosody was consistently the most difficult to recognize across groups. Nonverbal cognitive abilities is a significant predictor variable for successful recognition of neutral and happy prosody; although low nonverbal cognitive skills do not preclude minimally verbal children with ASD from accurately perceiving affective prosody. Conclusions: The present study shows it is possible for minimally verbal children with ASD to successfully participate in experimental research using judgment tasks when provided with appropriate training.

Emotion recognition in autism spectrum disorder

Proceedings of the ACM Symposium on Applied Perception, 2016

Figure 1: The emotion happiness shown by a female virtual character rendered realistic (left) and in different stylized variants.