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Found 8 papers, 0 papers with code
Federated Self-Supervised Learning for Acoustic Event Classification
Standard acoustic event classification (AEC) solutions require large-scale collection of data from client devices for model optimization.
Sentiment-Aware Automatic Speech Recognition pre-training for enhanced Speech Emotion Recognition
We pre-train SER model simultaneously on Automatic Speech Recognition (ASR) and sentiment classification tasks to make the acoustic ASR model more ``emotion aware''.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Contrastive Unsupervised Learning for Speech Emotion Recognition
Speech emotion recognition (SER) is a key technology to enable more natural human-machine communication.
Ranked #4 on
Speech Emotion Recognition
on MSP-Podcast (Dominance)
(using extra training data)
Compression of Acoustic Event Detection Models With Quantized Distillation
Acoustic Event Detection (AED), aiming at detecting categories of events based on audio signals, has found application in many intelligent systems.
Compression of Acoustic Event Detection Models with Low-rank Matrix Factorization and Quantization Training
In this paper, we present a compression approach based on the combination of low-rank matrix factorization and quantization training, to reduce complexity for neural network based acoustic event detection (AED) models.
Semi-supervised Acoustic Event Detection based on tri-training
This paper presents our work of training acoustic event detection (AED) models using unlabeled dataset.
Learning Spatiotemporal Features for Infrared Action Recognition with 3D Convolutional Neural Networks
Experimental results demonstrate that our approach can achieve state-of-the-art average precision (AP) performances on the InfAR dataset: (1) the proposed two-stream 3D CNN achieves the best reported 77. 5% AP, and (2) our 3D CNN model applied to the optical flow fields achieves the best reported single stream 75. 42% AP.
Learning Discriminative Features via Label Consistent Neural Network
Deep Convolutional Neural Networks (CNN) enforces supervised information only at the output layer, and hidden layers are trained by back propagating the prediction error from the output layer without explicit supervision.
