no code implementations • 21 Mar 2024 • Thejan Rajapakshe, Rajib Rana, Sara Khalifa, Berrak Sisman, Bjorn W. Schuller, Carlos Busso
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
no code implementations • 23 May 2023 • Thejan Rajapakshe, Rajib Rana, Sara Khalifa, Berrak Sisman, Björn Schuller
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)
no code implementations • 7 Jul 2022 • Thejan Rajapakshe, Rajib Rana, Sara Khalifa, Bjorn W. Schuller
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.
1 code implementation • 4 Jan 2021 • Thejan Rajapakshe, Rajib Rana, Sara Khalifa, Björn W. Schuller, Jiajun Liu
In addition, extended learning period is a general challenge for deep RL which can impact the speed of learning for SER.
no code implementations • 24 Oct 2019 • Thejan Rajapakshe, Rajib Rana, Siddique Latif, Sara Khalifa, Björn W. Schuller
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