An automatic speaker-speech recognition system for friendly HMI based on binary halved clustering

This work presents a low-cost and fast-trainable automatic speaker-speech recognition (ASSR) system, by proposed binary halved clustering (BHC) method for human-machine interface (HMI) on an embedded platform, owing to the trait of low cost in ASSR system is essential and affordable for real-world application. In addition, fast-trainable ability can provide fast responding time. The reduction of waiting time makes the proposed HMI to be friendly for users. The speech recognition uses enhanced cross-word reference templates (ECWRTs) for template training type. The novel BHC method uses binary-halved splitting to generate speaker models for low complexity requirement. The regularity of binary halved behavior is beneficial for data scheduling and resource sharing in the embedded ASSR system. Compared with the conventional works, simulation results indicate that the proposed hardware accelerator achieves 28% less cost, 90% less responding time, an ASSR accuracy of 90%. Comparison exhibits that performance of the proposed system is greater than the conventional works, thereby demonstrating the friendly and affordable factor of the proposed HMI.