Synthetic Data Augmentation for Speech Recognition

This field studies how to create extra training examples for speech recognizers when labeled recordings are limited or fail to represent real-world speakers and conditions. Common approaches include text-to-speech synthesis, voice conversion and cloning, language-model-generated transcripts or phonetic variants, and acoustic transformations such as speed or pitch changes. Researchers apply these methods to settings including low-resource languages, accents, code-switching, conversation, and disordered speech, and evaluate them with word error rates and task-specific error measures. A key open tension is that more natural-sounding synthetic data does not always improve recognition: its value depends on how well it matches the target speech, what variation it adds, and how it is mixed with real recordings.