Speech Model

Speech models aim to represent and process spoken language computationally, enabling applications like automatic speech recognition (ASR) and text-to-speech (TTS). Current research emphasizes improving model robustness (e.g., to noise and accents), fairness (mitigating biases against marginalized language varieties), and efficiency (through techniques like knowledge distillation and low-rank adaptation), often utilizing transformer-based architectures and self-supervised learning. These advancements have significant implications for various fields, including healthcare (e.g., voice disorder detection, mental health assessment), language preservation, and human-computer interaction.