BSC-UPC at EmoSPeech-IberLEF2024: Attention Pooling for Emotion Recognition
The domain of speech emotion recognition (SER) has persistently been a frontier within the landscape of machine learning. It is an active field that has been revolutionized in the last few decades and whose implementations are remarkable in multiple applications that could affect daily life. Consequently, the Iberian Languages Evaluation Forum (IberLEF) of 2024 held a competitive challenge to leverage the SER results with a Spanish corpus. This paper presents the approach followed with the goal of participating in this competition. The main architecture consists of different pre-trained speech and text models to extract features from both modalities, utilizing an attention pooling mechanism. The proposed system has achieved the first position in the challenge with an 86.69% in Macro F1-Score.
Code (1)
Tasks
Emotion RecognitionPositionSpeech Emotion RecognitionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
EmoSpeech: Guiding FastSpeech2 Towards Emotional Text to Speech
State-of-the-art speech synthesis models try to get as close as possible to the human voice. Hence, modelling emotions is an essential part of Text-To-Speech (TTS) research. In our work, we selected FastSpeech2 as the st…
Emotion RecognitionSpeech Synthesistext-to-speechText to SpeechIndian EmoSpeech Command Dataset: A dataset for emotion based speech recognition in the wild
Speech emotion analysis is an important task which further enables several application use cases. The non-verbal sounds within speech utterances also play a pivotal role in emotion analysis in speech. Due to the widespre…
Emotion RecognitionKeyword Spottingspeech-recognitionSpeech RecognitionSpeech Emotion Recognition Leveraging OpenAI's Whisper Representations and Attentive Pooling Methods
Speech Emotion Recognition (SER) research has faced limitations due to the lack of standard and sufficiently large datasets. Recent studies have leveraged pre-trained models to extract features for downstream tasks such …
Speech Emotion RecognitionEmotion Recognition with Spatial Attention and Temporal Softmax Pooling
Video-based emotion recognition is a challenging task because it requires to distinguish the small deformations of the human face that represent emotions, while being invariant to stronger visual differences due to diffe…
Emotion RecognitionDAGAM: A Domain Adversarial Graph Attention Model for Subject Independent EEG-Based Emotion Recognition
One of the most significant challenges of EEG-based emotion recognition is the cross-subject EEG variations, leading to poor performance and generalizability. This paper proposes a novel EEG-based emotion recognition mod…
EEGElectroencephalogram (EEG)Emotion RecognitionGraph Attention