Hybrid ASR for Resource-Constrained Robots: HMM - Deep Learning Fusion

TL;DR

This paper introduces a hybrid ASR system combining HMMs and deep learning for resource-limited robots, enabling real-time, accurate speech recognition through distributed processing and adaptability to various environments.

Contribution

It presents a novel hybrid architecture utilizing socket programming to distribute ASR processing between robot and PC, tailored for low-resource robotic platforms.

Findings

Enhanced speech recognition accuracy in resource-constrained environments

Real-time processing demonstrated on multiple robotic platforms

System adapts to changing acoustic conditions

Abstract

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.