p.6
Multilayer Perceptron (MLP) Classifier
What models are compared for speech emotion recognition in the study?
Multi-Layer Perceptron (MLP) and Convolutional Neural Network Long Short-Term Memory (CNN LSTM).
p.6
Future Scope and Limitations of SER Models
What additional cues could enhance emotion detection accuracy?
Facial expressions and gestures.
p.6
Challenges in Speech Emotion Recognition
Why is sarcasm difficult to detect with MLP and CNN LSTM models?
Because they need to understand the context.
p.2
Machine Learning Algorithms in SER
What method did Seyedmahdad Mirsamadi et al. use for speech emotion recognition?
Recurrent Neural Networks.
p.5
Performance Metrics and Accuracy Comparison
What visual representations are mentioned for CNN LSTM's performance?
Confusion matrix and performance metrics.
p.2
Challenges in Speech Emotion Recognition
What is one of the main challenges in speech emotion recognition?
Dealing with changes in emotional expression across different speakers.
p.2
RAVDESS Dataset Utilization
How are the actors represented in the RAVDESS dataset?
12 male actors with odd numbers and 12 female actors with even numbers.
p.3
Speech Emotion Recognition (SER)
What is the main goal of the research paper?
To recognize emotions in speech.
p.1
Feature Extraction Techniques (MFCC and Mel Spectrogram)
What speech parameters are used to extract emotions?
Mel-Frequency-Cepstral Coefficients (MFCC) and Mel Spectrogram.
p.1
Speech Emotion Recognition (SER)
What is the process of extracting emotions from human speech called?
Speech Emotion Recognition (SER).
p.3
Feature Extraction Techniques (MFCC and Mel Spectrogram)
What does the Mel scale relate?
The perceived frequency of a tone to the real measured frequency.
p.4
Feature Extraction Techniques (MFCC and Mel Spectrogram)
Which techniques are used for Exploratory Data Analysis in this research?
MFCC and Mel Spectrogram.
p.4
Data Augmentation Techniques
What is the purpose of data augmentation in this study?
To make the model insensitive to disturbances and improve its generalizability.
p.3
Multilayer Perceptron (MLP) Classifier
What activation function is used in the hidden layer of the MLP?
Rectified linear unit activation function.
p.1
RAVDESS Dataset Utilization
Which dataset was used to extract emotions in the study?
RAVDESS (Ryerson Audio-Visual Database of Emotional Speech and Song).
p.4
Convolutional Neural Networks Long Short Term Memory (CNN LSTM)
What are the two distinct components of the model used for Speech Emotion Recognition?
The CNN Model for feature extraction and the LSTM Model for analyzing extracted features.
p.1
Challenges in Speech Emotion Recognition
Why is recognizing emotions from speech important?
It provides insights into a person's thoughts and is crucial for effective communication.
p.5
Speech Emotion Recognition (SER)
How many classes of emotions are identified in the research?
16 classes, with 8 classes for female emotions and 8 classes for male emotions.
p.4
Machine Learning Algorithms in SER
What is the default split ratio for the dataset in Speech Emotion Recognition?
70% for training and 30% for testing.
p.3
Multilayer Perceptron (MLP) Classifier
What are the three types of layers in a Multi Layer Perceptron?
Input layer, hidden layer, and output layer.
p.1
Machine Learning Algorithms in SER
What machine learning algorithms were compared in the study?
Multilayer Perceptron (MLP) and Convolutional Neural Networks Long Short Term Memory (CNN LSTM).
p.6
Challenges in Speech Emotion Recognition
What limitation do MLP and CNN LSTM models have in emotion detection?
They do not consider contextual information.
p.6
Convolutional Neural Networks Long Short Term Memory (CNN LSTM)
What is the conclusion of the study regarding the CNN LSTM model?
It is a promising approach for speech emotion recognition tasks.
p.2
Machine Learning Algorithms in SER
What approach did Chi-Chun Lee et al. propose for emotion recognition?
Hierarchical Binary Decision Tree.
p.1
Multilayer Perceptron (MLP) Classifier
What classifier achieved an accuracy of 68.33% in the study?
Multilayer Perceptron (MLP).
p.1
Challenges in Speech Emotion Recognition
What role do emotions play in sensitive professions?
They help describe how a person is feeling and their state of mind.
p.3
Convolutional Neural Networks Long Short Term Memory (CNN LSTM)
What does CNN LSTM combine?
CNN layers and LSTM layers for sequence prediction.
p.4
Convolutional Neural Networks Long Short Term Memory (CNN LSTM)
How many one-dimensional convolutional layers are used in the CNN LSTM implementation?
Three one-dimensional convolutional layers.
p.3
Feature Extraction Techniques (MFCC and Mel Spectrogram)
What technique is used for feature extraction in audio data?
MFCC (Mel Frequency Cepstral Coefficients).
p.3
Feature Extraction Techniques (MFCC and Mel Spectrogram)
What is a Mel Spectrogram?
A visual representation of signal strength and frequency of sound waves.
p.2
Future Scope and Limitations of SER Models
What is the aim of the project discussed in the paper?
To develop a system that can accurately recognize the emotional state of a speaker based on their speech signal.
p.4
Convolutional Neural Networks Long Short Term Memory (CNN LSTM)
What activation function is used for the convolutional layers in the CNN LSTM model?
Rectified Linear Unit (ReLU).
