README.md

References

• The INTERSPEECH 2018 Computational Paralinguistics Challenge: Atypical & Self-Assessed Affect, Crying & Heart Beats

• Deep Learning in Paralinguistic Recognition Tasks: Are Hand-crafted Features Still Relevant?

• Investigation on Joint Representation Learning for Robust Feature Extraction in Speech Emotion Recognition

• Using Voice Quality Supervectors for Affect Identification

• An End-to-End Deep Learning Framework for Speech Emotion Recognition of Atypical Individuals

• Attention-based Sequence Classification for Affect Detection - Implemented the attention part. Data balancing part of the paper seems interesting to try out.

• Computational Paralinguistics: Automatic Assessment of Emotions, Mood and Behavioural State from Acoustics of Speech

• Investigating Utterance Level Representations for Detecting Intent from Acoustics

• LSTM Based Cross-corpus and Cross-task Acoustic Emotion Recognition

• Implementing Fusion Techniques for the Classification of Paralinguistic Information

• General Utterance-Level Feature Extraction for Classifying Crying Sounds, Atypical & Self-Assessed Affect and Heart Beats

• Self-Assessed Affect Recognition Using Fusion of Attentional BLSTM and Static Acoustic Features

• Vocalic, Lexical and Prosodic Cues for the INTERSPEECH 2018 Self-Assessed Affect Challenge