Speech emotion recognition with deep convolutional neural networks
The speech emotion recognition (or, classification) is one of the most challenging topics in
data science. In this work, we introduce a new architecture, which extracts mel-frequency …
data science. In this work, we introduce a new architecture, which extracts mel-frequency …
Speech emotion recognition with co-attention based multi-level acoustic information
Speech Emotion Recognition (SER) aims to help the machine to understand human's
subjective emotion from only audio in-formation. However, extracting and utilizing …
subjective emotion from only audio in-formation. However, extracting and utilizing …
Survey of deep representation learning for speech emotion recognition
Traditionally, speech emotion recognition (SER) research has relied on manually
handcrafted acoustic features using feature engineering. However, the design of …
handcrafted acoustic features using feature engineering. However, the design of …
[HTML][HTML] Speech emotion recognition using fusion of three multi-task learning-based classifiers: HSF-DNN, MS-CNN and LLD-RNN
Speech emotion recognition plays an increasingly important role in emotional computing
and is still a challenging task due to its complexity. In this study, we developed a framework …
and is still a challenging task due to its complexity. In this study, we developed a framework …
Wavoice: A noise-resistant multi-modal speech recognition system fusing mmwave and audio signals
With the advance in automatic speech recognition, voice user interface has gained
popularity recently. Since the COVID-19 pandemic, VUI is increasingly preferred in online …
popularity recently. Since the COVID-19 pandemic, VUI is increasingly preferred in online …
Combining a parallel 2D CNN with a self-attention Dilated Residual Network for CTC-based discrete speech emotion recognition
A challenging issue in the field of the automatic recognition of emotion from speech is the
efficient modelling of long temporal contexts. Moreover, when incorporating long-term …
efficient modelling of long temporal contexts. Moreover, when incorporating long-term …
Designing and Evaluating Speech Emotion Recognition Systems: A reality check case study with IEMOCAP
There is an imminent need for guidelines and standard test sets to allow direct and fair
comparisons of speech emotion recognition (SER). While resources, such as the Interactive …
comparisons of speech emotion recognition (SER). While resources, such as the Interactive …
Head fusion: Improving the accuracy and robustness of speech emotion recognition on the IEMOCAP and RAVDESS dataset
Speech Emotion Recognition (SER) refers to the use of machines to recognize the emotions
of a speaker from his (or her) speech. SER benefits Human-Computer Interaction (HCI). But …
of a speaker from his (or her) speech. SER benefits Human-Computer Interaction (HCI). But …
Speech emotion recognition with multiscale area attention and data augmentation
In Speech Emotion Recognition (SER), emotional characteristics often appear in diverse
forms of energy patterns in spectrograms. Typical attention neural network classifiers of SER …
forms of energy patterns in spectrograms. Typical attention neural network classifiers of SER …
Isnet: Individual standardization network for speech emotion recognition
Speech emotion recognition plays an essential role in human-computer interaction.
However, cross-individual representation learning and individual-agnostic systems are …
However, cross-individual representation learning and individual-agnostic systems are …