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Found 6 papers, 1 papers with code
emoDARTS: Joint Optimisation of CNN & Sequential Neural Network Architectures for Superior Speech Emotion Recognition
This study presents emoDARTS, a DARTS-optimised joint CNN and Sequential Neural Network (SeqNN: LSTM, RNN) architecture that enhances SER performance.
Ranked #1 on
Speech Emotion Recognition
on MSP-IMPROV
Enhancing Speech Emotion Recognition Through Differentiable Architecture Search
In contrast to previous studies, we refrain from imposing constraints on the order of the layers for the CNN within the DARTS cell; instead, we allow DARTS to determine the optimal layer order autonomously.
Ranked #5 on
Speech Emotion Recognition
on IEMOCAP
(UA metric)
Domain Adapting Deep Reinforcement Learning for Real-world Speech Emotion Recognition
Evaluation results show that in a live data feed setting, RL-DA outperforms a baseline strategy by 11% and 14% in cross-corpus and cross-language scenarios, respectively.
A novel policy for pre-trained Deep Reinforcement Learning for Speech Emotion Recognition
In addition, extended learning period is a general challenge for deep RL which can impact the speed of learning for SER.
Deep Representation Learning in Speech Processing: Challenges, Recent Advances, and Future Trends
Research on speech processing has traditionally considered the task of designing hand-engineered acoustic features (feature engineering) as a separate distinct problem from the task of designing efficient machine learning (ML) models to make prediction and classification decisions.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Pre-training in Deep Reinforcement Learning for Automatic Speech Recognition
Deep reinforcement learning (deep RL) is a combination of deep learning with reinforcement learning principles to create efficient methods that can learn by interacting with its environment.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
