Hybrid TDNN

Hybrid TDNNs are a prominent architecture in automatic speech recognition (ASR), aiming to improve accuracy and efficiency. Current research focuses on optimizing these models through techniques like neural architecture search to automatically determine optimal network configurations, mixed-precision quantization to reduce model size and computational cost without significant performance loss, and system combination strategies that leverage the complementary strengths of hybrid TDNNs and end-to-end models like Conformers. These advancements contribute to creating more robust, resource-efficient ASR systems with potential for broader deployment in practical applications.