how to augument speech sentiment dataset?
Your problem is to identify whether a person is sad, happy, angry or neutral from his voice which is emotion classification problem. For speech, we use frames of short duration like 10-20 ms, and extract features from the same like MFCC or other frequency domain features. We extract short duration frames because speech is non-stationary over time, and frequency domain features make the features shift-invariant.

I suggest you read some latest research papers on emotion classification to get latest research in emotion classification.

Emotion in speech is captured by variation in pitch and amplitude across time. So capturing short time energy and instantaneous pitch frequency in speech across time  are basic features for emotion classification.

Modifying speech by adding variation in volume and white noise will not help at all.

You need to build a emotion classification system with 4 classes: sad, happy, angry or neutral, using the below steps:


Extract frames of 10-30 ms duration
Convert these frames to frequency domain features like STFT or extract instantaneous pitch
Use these STFT features along with pitch to train a LSTM