scholarly journals IMPROVED SPEAKER-INDEPENDENT EMOTION RECOGNITION FROM SPEECH USING TWO-STAGE FEATURE REDUCTION

2015 ◽  
Author(s):  
Hasrul Mohd Nazid ◽  
Hariharan Muthusamy ◽  
Vikneswaran Vijean ◽  
Sazali Yaacob
Keyword(s):  
Emotion Recognition ◽  
Principal Component ◽  
Feature Reduction ◽  
Speech Emotion Recognition ◽  
Emotional Speech ◽  
Two Stage ◽  
Linear Discriminant ◽  
Speaker Independent ◽  
Speech Features ◽  
And Gender

In the recent years, researchers are focusing to improve the accuracy of speech emotion recognition. Generally, high emotion recognition accuracies were obtained for two-class emotion recognition, but multi-class emotion recognition is still a challenging task . The main aim of this work is to propose a two-stage feature reduction using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) for improving the accuracy of the speech emotion recognition (ER) system. Short-term speech features were extracted from the emotional speech signals. Experiments were carried out using four different supervised classifi ers with two different emotional speech databases. From the experimental results, it can be inferred that the proposed method provides better accuracies of 87.48% for speaker dependent (SD) and gender dependent (GD) ER experiment, 85.15% for speaker independent (SI) ER experiment, and 87.09% for gender independent (GI) experiment.  

scholarly journals IMPROVED SPEAKER-INDEPENDENT EMOTION RECOGNITION FROM SPEECH USING TWO-STAGE FEATURE REDUCTION

2015 ◽  
Vol 14 ◽  
pp. 57-76
Author(s):  
Hasrul Mohd Nazid ◽  
Hariharan Muthusamy ◽  
Vikneswaran Vijean ◽  
Sazali Yaacob
Keyword(s):  
Emotion Recognition ◽  
Feature Reduction ◽  
Two Stage ◽  
Speaker Independent

scholarly journals Impact of Feature Selection Algorithm on Speech Emotion Recognition Using Deep Convolutional Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6008 ◽  
Author(s):  
Misbah Farooq ◽  
Fawad Hussain ◽  
Naveed Khan Baloch ◽  
Fawad Riasat Raja ◽  
Heejung Yu ◽  
...  
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Convolutional Neural Network ◽  
Emotion Recognition ◽  
Deep Convolutional Neural Network ◽  
Speech Emotion Recognition ◽  
Support Vector ◽  
Emotional Speech ◽  
Human Machine Interaction ◽  
Speaker Independent

Speech emotion recognition (SER) plays a significant role in human–machine interaction. Emotion recognition from speech and its precise classification is a challenging task because a machine is unable to understand its context. For an accurate emotion classification, emotionally relevant features must be extracted from the speech data. Traditionally, handcrafted features were used for emotional classification from speech signals; however, they are not efficient enough to accurately depict the emotional states of the speaker. In this study, the benefits of a deep convolutional neural network (DCNN) for SER are explored. For this purpose, a pretrained network is used to extract features from state-of-the-art speech emotional datasets. Subsequently, a correlation-based feature selection technique is applied to the extracted features to select the most appropriate and discriminative features for SER. For the classification of emotions, we utilize support vector machines, random forests, the k-nearest neighbors algorithm, and neural network classifiers. Experiments are performed for speaker-dependent and speaker-independent SER using four publicly available datasets: the Berlin Dataset of Emotional Speech (Emo-DB), Surrey Audio Visual Expressed Emotion (SAVEE), Interactive Emotional Dyadic Motion Capture (IEMOCAP), and the Ryerson Audio Visual Dataset of Emotional Speech and Song (RAVDESS). Our proposed method achieves an accuracy of 95.10% for Emo-DB, 82.10% for SAVEE, 83.80% for IEMOCAP, and 81.30% for RAVDESS, for speaker-dependent SER experiments. Moreover, our method yields the best results for speaker-independent SER with existing handcrafted features-based SER approaches.

scholarly journals Speech Emotion Recognition: Humans vs Machines

Discourse ◽  
2019 ◽  
Vol 5 (5) ◽  
pp. 136-152
Author(s):  
S. Werner ◽  
G. N. Petrenko
Keyword(s):  
Emotion Recognition ◽  
Native Speakers ◽  
Musical Training ◽  
Speech Emotion Recognition ◽  
Emotional Speech ◽  
Emotion Classification ◽  
Language Technology ◽  
Language Studies ◽  
And Gender ◽  
Big Six

Introduction. The study focuses on emotional speech perception and speech emotion recognition using prosodic clues alone. Theoretical problems of defining prosody, intonation and emotion along with the challenges of emotion classification are discussed. An overview of acoustic and perceptional correlates of emotions found in speech is provided. Technical approaches to speech emotion recognition are also considered in the light of the latest emotional speech automatic classification experiments.Methodology and sources. The typical “big six” classification commonly used in technical applications is chosen and modified to include such emotions as disgust and shame. A database of emotional speech in Russian is created under sound laboratory conditions. A perception experiment is run using Praat software’s experimental environment.Results and discussion. Cross-cultural emotion recognition possibilities are revealed, as the Finnish and international participants recognised about a half of samples correctly. Nonetheless, native speakers of Russian appear to distinguish a larger proportion of emotions correctly. The effects of foreign languages knowledge, musical training and gender on the performance in the experiment were insufficiently prominent. The most commonly confused pairs of emotions, such as shame and sadness, surprise and fear, anger and disgust as well as confusions with neutral emotion were also given due attention.Conclusion. The work can contribute to psychological studies, clarifying emotion classification and gender aspect of emotionality, linguistic research, providing new evidence for prosodic and comparative language studies, and language technology, deepening the understanding of possible challenges for SER systems.

scholarly journals Fusing Visual Attention CNN and Bag of Visual Words for Cross-Corpus Speech Emotion Recognition

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5559
Author(s):  
Minji Seo ◽  
Myungho Kim
Keyword(s):  
Visual Attention ◽  
Emotion Recognition ◽  
Expressed Emotion ◽  
Local Features ◽  
Speech Emotion Recognition ◽  
Bag Of Visual Words ◽  
Emotional Speech ◽  
Visual Words ◽  
Performance Reduction ◽  
Global And Local

Speech emotion recognition (SER) classifies emotions using low-level features or a spectrogram of an utterance. When SER methods are trained and tested using different datasets, they have shown performance reduction. Cross-corpus SER research identifies speech emotion using different corpora and languages. Recent cross-corpus SER research has been conducted to improve generalization. To improve the cross-corpus SER performance, we pretrained the log-mel spectrograms of the source dataset using our designed visual attention convolutional neural network (VACNN), which has a 2D CNN base model with channel- and spatial-wise visual attention modules. To train the target dataset, we extracted the feature vector using a bag of visual words (BOVW) to assist the fine-tuned model. Because visual words represent local features in the image, the BOVW helps VACNN to learn global and local features in the log-mel spectrogram by constructing a frequency histogram of visual words. The proposed method shows an overall accuracy of 83.33%, 86.92%, and 75.00% in the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), the Berlin Database of Emotional Speech (EmoDB), and Surrey Audio-Visual Expressed Emotion (SAVEE), respectively. Experimental results on RAVDESS, EmoDB, SAVEE demonstrate improvements of 7.73%, 15.12%, and 2.34% compared to existing state-of-the-art cross-corpus SER approaches.

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