This paper presents a novel hybrid Automatic Speech Recognition (ASR) system
designed specifically for resource-constrained robots. The proposed approach
combines Hidden Markov Models (HMMs) with deep learning models and leverages
socket programming to distribute processing tasks effectively. In this
architecture, the HMM-based processing takes place within the robot, while a
separate PC handles the deep learning model. This synergy between HMMs and deep
learning enhances speech recognition accuracy significantly. We conducted
experiments across various robotic platforms, demonstrating real-time and
precise speech recognition capabilities. Notably, the system exhibits
adaptability to changing acoustic conditions and compatibility with low-power
hardware, making it highly effective in environments with limited computational
resources. This hybrid ASR paradigm opens up promising possibilities for
seamless human-robot interaction. In conclusion, our research introduces a
pioneering dimension to ASR techniques tailored for robotics. By employing
socket programming to distribute processing tasks across distinct devices and
strategically combining HMMs with deep learning models, our hybrid ASR system
showcases its potential to enable robots to comprehend and respond to spoken
language adeptly, even in environments with restricted computational resources.
This paradigm sets a innovative course for enhancing human-robot interaction
across a wide range of real-world scenarios.Comment: To be published in IEEE Access, 9 pages, 14 figures, Received
valuable support from CCBD PESU, for associated code, see
https://github.com/AnshulRanjan2004/PyHM