Speech emotion recognition with co-attention based multi-level acoustic information[C]//International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022: 7367-7371. [2]Baevski A, Zhou Y, Mohamed A, et al. wav2vec 2.0: A framework for self-supervised learning of ...
In this paper, we propose an end-to-end speech emotion recognition system using multi-level acoustic information with a newly designed co-attention module. We firstly extract multi-level acoustic information, including MFCC, spectrogram, and the embedded high-level acoustic information with CNN, BiL...
Specifically, a cross-attention fusion (CAF) module is designed to integrate the dual-stream output for emotion recognition. Using different dual-stream encoders (fully training a text processing network and fine-tuning a pre-trained large language network), the CAF module ...
Using Convolutional Neural Networks in speech emotion recognition on the RAVDESS Audio Dataset. emotionaudio-filescnn-modelspeech-emotion-recognition UpdatedApr 12, 2021 Jupyter Notebook Code for Speech Emotion Recognition with Co-Attention based Multi-level Acoustic Information ...
摘要原文 The paper presents a Multi-Head Attention deep learning network for Speech Emotion Recognition (SER) using Log mel-Filter Bank Energies (LFBE) spectral features as the input. The multi-head attention along with the position embedding jointly attends to information from different representatio...
The experimental results indicated the significance and the efficiency of our proposed model have shown excessive assistance with the implementation of a real-time SER system. Hence, our model is capable of processing original speech signals for the emotion recognition that utilizes lightweight dilated ...
BiLSTM is used to solve the problem of poor performance of long-term dependent learning features, and attention mechanism is used for only a few frames contain emotional features in the children speech signal. Compared with the related speech emotion recognition models such as LSTM-CNN and 2D-...
CNN+LSTM Architecture for Speech Emotion Recognition with Data Augmentation In this work we design a neural network for recognizing emotions in speech,\nusing the IEMOCAP dataset. Following the latest advances in audio analysis, we\nuse an architecture involving both convolutional layers, for ...
In the field of Human-Computer Interaction (HCI), Speech Emotion Recognition (SER) is not only a fundamental step towards intelligent interaction but also plays an important role in smart environments e.g., elderly home monitoring. Most deep learning based SER systems invariably focus on handling...
SCC-MPGCN: Self-Attention Coherence Clustering Based on Multi-Pooling Graph Convolutional Network for EEG Emotion Recognition The emotion recognition with electroencephalography (EEG) has been widely studied using the deep learning methods, but the topology of EEG channels is rare... H Zhao,J Liu...