Chapter 3

Improving Quality and Quantity of Training Data

When is noise in your data a good thing? When it accurately reflects real-world conditions.

For speech and voice applications, typical existing large data sets will be recorded in ways that differ from real application scenarios. If your application is supposed to recognize a spoken trigger word, then it needs to cope with poor microphones, specific types of reverberation, and background noise.

These and other effects can be added artificially to grow a training data set using established signal processing methods and domain-specific applications through:

• Data augmentation
• Data synthesis

Signals can be difficult to measure consistently or observe to build a large data set; this chapter looks at techniques to create more training data. Data synthesis can help create new signals from models or simulations, and data augmentation is a specific type of data synthesis that creates new variations of your existing data.

First, a brief overview of how deep learning works with signal data.

Data Augmentation

Starting from existing labeled samples, augmentation generates:

• Training data that is similar to your high-quality validation data
• Variations of the available data that the system may encounter in real-world scenarios

Augmentation effects are often domain specific. Common augmentation effects for audio, speech, and acoustic data include stretch time, shift pitch, control volume, and many more.