Voice Emotion Recognition
1. Introduction
Voice Emotion Recognition is a project that identifies emotions from voice inputs using audio processing and Natural Language Processing (NLP). By analyzing speech patterns, tone, pitch, and other audio features, this system can detect emotions such as happiness, sadness, anger, and more. It is widely applicable in areas such as mental health assessment, customer service, and human-computer interaction.
2. Prerequisites
• Python: Install Python 3.x from the official Python
website.
• Required Libraries:
- librosa: Install using pip install
librosa
- sklearn: Install using pip install
scikit-learn
- keras: Install using pip install
keras
- tensorflow: Install using pip install
tensorflow
- numpy and pandas: Install using pip
install numpy pandas
- matplotlib: Install using pip install
matplotlib
• Dataset: Emotion-labeled audio datasets (e.g., RAVDESS, Emo-DB, or custom
datasets).
3. Project Setup
1. Create a Project Directory:
- Name your project folder, e.g.,
`Voice_Emotion_Recognition`.
- Inside this folder, create the Python script file (`emotion_recognition.py`).
2. Install Required Libraries:
Ensure Librosa, TensorFlow, and other dependencies are installed using `pip`.
4. Writing the Code
Below is an example code snippet for Voice Emotion Recognition:
import librosa
import numpy as np
import os
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from keras.models import Sequential
from keras.layers import Dense, Dropout, LSTM
# Function to extract audio features
def extract_features(file_path):
audio, sample_rate =
librosa.load(file_path, res_type='kaiser_fast')
mfccs = librosa.feature.mfcc(y=audio,
sr=sample_rate, n_mfcc=40)
return np.mean(mfccs.T, axis=0)
# Load dataset
data_path = "path_to_audio_dataset"
# Replace with your dataset path
labels, features = [], []
for folder in os.listdir(data_path):
for file in
os.listdir(os.path.join(data_path, folder)):
try:
feature =
extract_features(os.path.join(data_path, folder, file))
features.append(feature)
labels.append(folder)
except:
continue
# Encode labels
label_encoder = LabelEncoder()
y = label_encoder.fit_transform(labels)
X = np.array(features)
y = np.array(y)
# Train-test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,
random_state=42)
# Build the model
model = Sequential()
model.add(Dense(256, input_shape=(X_train.shape[1],), activation='relu'))
model.add(Dropout(0.3))
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.3))
model.add(Dense(len(set(y)), activation='softmax'))
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
# Train the model
model.fit(X_train, y_train, epochs=50, batch_size=32, validation_data=(X_test,
y_test))
5. Key Components
• Audio Feature Extraction: Extracts features such as MFCCs
from audio inputs.
• Model Architecture: Employs a neural network for classifying emotions based
on extracted features.
• Label Encoding: Converts emotion labels into numerical formats for model
training.
6. Testing
1. Ensure the audio dataset is properly labeled and organized.
2. Run the script:
python emotion_recognition.py
3. Verify the model's accuracy on test data.
7. Enhancements
• Advanced Models: Use deep learning architectures like CNNs
or transformers for better accuracy.
• Real-Time Analysis: Integrate the system with microphones for real-time
emotion detection.
• Multimodal Inputs: Combine text and audio inputs for enhanced emotion
recognition.
8. Troubleshooting
• Poor Accuracy: Use a larger dataset or fine-tune
hyperparameters.
• Data Processing Errors: Ensure audio files are in the correct format and
sample rate.
• Overfitting: Implement regularization techniques like dropout.
9. Conclusion
The Voice Emotion Recognition system utilizes audio and NLP techniques to detect emotions. This project has diverse applications, from enhancing human-computer interactions to improving mental health diagnostics.