Voice Emotion Recognition

 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.