Emotion, age, and gender classification in children's speech by humans and machines. (November 2017)

• Record Type: Journal Article
• Authors: Kaya, Heysem; Salah, Albert Ali; Karpov, Alexey; Frolova, Olga; Grigorev, Aleksey; Lyakso, Elena
• Is Part Of: Computer speech & language. Volume 46(2017)
• Page Start: 268
• Page End: 283
• Publication Date: 2017-11
• Subjects: Emotional child speech -- Perception experiments -- Spectrographic analysis -- Emotional states -- Age recognition -- Gender recognition -- Computational paralinguistics; Speech processing systems -- Periodicals; Automatic speech recognition -- Periodicals; Computers -- Periodicals; Linguistics -- Periodicals; Speech-Language Pathology -- Periodicals; Traitement automatique de la parole -- Périodiques; Reconnaissance automatique de la parole -- Périodiques; Automatic speech recognition; Speech processing systems; Electronic journals; Periodicals; 006.454
• DOI: 10.1016/j.csl.2017.06.002
• Languages: English
• ISSNs: 0885-2308

Abstract

Abstract: In this article, we present the first child emotional speech corpus in Russian, called "EmoChildRu", collected from 3 to 7 years old children. The base corpus includes over 20 K recordings (approx. 30 h), collected from 120 children. Audio recordings are carried out in three controlled settings by creating different emotional states for children: playing with a standard set of toys; repetition of words from a toy-parrot in a game store setting; watching a cartoon and retelling of the story, respectively. This corpus is designed to study the reflection of the emotional state in the characteristics of voice and speech and for studies of the formation of emotional states in ontogenesis. A portion of the corpus is annotated for three emotional states (comfort, discomfort, neutral). Additional data include the results of the adult listeners' analysis of child speech, questionnaires, as well as annotation for gender and age in months. We also provide several baselines, comparing human and machine estimation on this corpus for prediction of age, gender and comfort state. While in age estimation, the acoustics-based automatic systems show higher performance, they do not reach human perception levels in comfort state and gender classification. The comparative results indicate the importance and necessity of developing further linguistic models for discrimination.