Accent Adaptation

Accent adaptation in speech processing focuses on improving the robustness of systems like automatic speech recognition (ASR) and text-to-speech (TTS) to variations in speaker accents. Current research emphasizes developing techniques that leverage self-supervised learning, incorporating accent-specific information into model architectures (e.g., using codebooks or adapter layers), and employing meta-learning for efficient adaptation across multiple accents. These advancements are crucial for broader accessibility and improved performance of speech technologies, particularly benefiting under-resourced languages and dialects.