How to implement speech emotion recognition and identify the emotions and feelings in speech?

Speech emotion recognition (SER) involves analyzing audio signals to detect emotions like happiness, anger, sadness, or excitement. Here's how to implement it:

1. Data Collection: Gather labeled speech datasets (e.g., RAVDESS, CREMA-D) containing audio clips with emotion annotations.
2. Preprocessing: Clean audio data by removing noise, normalizing volume, and extracting features like MFCCs (Mel-Frequency Cepstral Coefficients), pitch, and energy.
3. Feature Extraction: Use libraries like Librosa to compute acoustic features that correlate with emotions.
4. Model Training: Train machine learning models (e.g., SVM, CNN, or LSTM) on the extracted features. Deep learning models like CRNNs (Convolutional Recurrent Neural Networks) are effective for capturing temporal patterns.
5. Evaluation: Test the model on a validation set using metrics like accuracy, F1-score, or confusion matrices.

Example: A customer service system uses SER to detect frustration in a caller's voice and escalates the call to a human agent.

For scalable deployment, Tencent Cloud offers Intelligent Speech Recognition (ISR) and Machine Learning Platform for AI, which can streamline SER model training and inference.