Exploring the impact of noise, language familiarity, and experimental settings on emotion recognition - Amorese, Terry; Cuciniello, Marialucia; Alterio, Anna; Pepe, Daniele; Scharenborg, Odette; Cordasco, Gennaro; Esposito, Anna (Frontiers in Psychology, 2025)
IntroductionThis work aims to understand the contextual factors affecting speech emotion recognition (SER), more specifically the current research investigates whether the identification of vocal emotional expressions of anger, fear, sadness, joy, and neutrality is affected by three factors: (a) the experimental setting, exploring vocal emotion recognition in both a controlled, soundproof laboratory and a more natural listening environment; (b) the effect of stimuli’s background noise: sentences were presented with three different levels of noise to gradually increase the level of difficulty: one clear (no noise) condition and two noise conditions; (c) language familiarity, since the stimuli comprised Italian sentences, and participants were both native (Italians) and Dutch speakers, who did not know Italian.MethodDutch and Italian participants were involved in a vocal emotion recognition task carried out in two different experimental settings (realistic vs. laboratory). The stimuli were vocal utterances from the Italian EMOVO dataset, conveying emotions like anger, fear, sadness, joy, and neutrality, and were presented in three different noise conditions.ResultsConcerning the effect of the experimental setting, even in higher levels of background noise conditions, individuals possess the remarkable ability to discern emotional nuances conveyed through voice. Regarding familiarity with the language, differences in emotion recognition performance between the Italian and Dutch listeners were observed, but the error magnitude was contingent on the emotional categories. Higher noise levels reduced accuracy, but people could still discern emotions, especially prosody.ConclusionThe study highlighted that emotion recognition is influenced by variables such as listening context, background noise, and language familiarity. These results could be useful for developing robust Speech Emotion Recognition (SER) systems and improving human-computer interaction.