TY - GEN
T1 - Speech Emotion Recognition Using Convolutional Recurrent Neural Networks
AU - Gangani, Abhishek
AU - Zhang, Li
AU - Jiang, Ming
PY - 2024/7/30
Y1 - 2024/7/30
N2 - Research suggests that various machine learning and deep learning models can be used for implementation of speech emotion recognition (SER) using different acoustic properties, such as voice, pitch, loudness, intensity, Mel-frequency cepstral coefficients, and spectral characteristics. This chapter conducts speech emotion recognition using deep neural networks, such as long short-term memory, gated recurrent units, and convolutional recurrent neural network. These different acoustic features are investigated in our studies owing to their great efficiency in representing key events in audio representations. A cross-validation evaluation has been conducted with the data from different actors for model evaluation to check the robustness of each proposed network. The proposed models show impressive performances in comparison with those of existing state-of-the-art methods for evaluating several speech emotion datasets.
AB - Research suggests that various machine learning and deep learning models can be used for implementation of speech emotion recognition (SER) using different acoustic properties, such as voice, pitch, loudness, intensity, Mel-frequency cepstral coefficients, and spectral characteristics. This chapter conducts speech emotion recognition using deep neural networks, such as long short-term memory, gated recurrent units, and convolutional recurrent neural network. These different acoustic features are investigated in our studies owing to their great efficiency in representing key events in audio representations. A cross-validation evaluation has been conducted with the data from different actors for model evaluation to check the robustness of each proposed network. The proposed models show impressive performances in comparison with those of existing state-of-the-art methods for evaluating several speech emotion datasets.
UR - https://www.worldscientific.com/doi/10.1142/9789811294631_0036
U2 - 10.1142/9789811294631_0036
DO - 10.1142/9789811294631_0036
M3 - Conference contribution
SN - 978-981-12-9462-4
T3 - World Scientific Proceedings Series on Computer Engineering and Information Science
SP - 283
EP - 290
BT - Intelligent Management of Data and Information in Decision Making
ER -