TTS augmentation is a machine learning technique in speech processing where text-to-speech synthesis systems are used to generate synthetic audio from text to expand training datasets. In automatic speech recognition and related audio tasks, models typically require large volumes of paired audio recordings and text transcriptions, which are often scarce or costly to collect for low-resource languages, dialects, or specialized vocabularies. By converting text-only sources into synthetic spoken utterances, TTS augmentation artificially produces labeled audio-text pairs. This approach increases the volume and diversity of training data, helping to improve the accuracy, generalization, and acoustic robustness of speech processing models without solely relying on manually recorded human speech.
