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Speech emotion recognition is a challenging task, and extensive reliance has been placed on models that use audio features in building well-performing classifiers.
In this paper, we present a database of emotional speech intended to be open-sourced and used for synthesis and generation purpose.
In this work, we adopt a feature-engineering based approach to tackle the task of speech emotion recognition.
SOTA for Speech Emotion Recognition on IEMOCAP (F1 metric )
Cross-lingual speech emotion recognition is an important task for practical applications.
In this paper, we present a novel attention based fully convolutional network for speech emotion recognition.
In this work, we explore the impact of visual modality in addition to speech and text for improving the accuracy of the emotion detection system.
In this work, we propose an interaction-aware attention network (IAAN) that incorporate contextual information in the learned vocal representation through a novel attention mechanism.
Despite the increasing research interest in end-to-end learning systems for speech emotion recognition, conventional systems either suffer from the overfitting due in part to the limited training data, or do not explicitly consider the different contributions of automatically learnt representations for a specific task.
The field of Text-to-Speech has experienced huge improvements last years benefiting from deep learning techniques.