Speech Transformer

Speech transformers are neural network architectures leveraging self-attention mechanisms to process and understand speech data, primarily aiming to improve the accuracy and efficiency of tasks like automatic speech recognition (ASR), speaker verification, and text-to-speech (TTS). Current research focuses on optimizing transformer models for efficiency, including exploring techniques like knowledge distillation, attention map reuse, and the integration of convolutional modules to reduce computational costs while maintaining performance. These advancements are significant because they enable the deployment of high-performing speech processing systems on resource-constrained devices and improve the robustness of these systems to noisy or diverse speech inputs, impacting fields ranging from virtual assistants to clinical applications.