Research on Digital Human Speech Recognition Method in High-Disturbance Industrial Environment

The advent of industrial robotics and speech technology has precipitated a paradigm shift in the manner in which humans and machines collaborate. This article investigates the application and optimization of digital human speech recognition technology in industrial contexts, with a particular focus on scenarios characterized by significant disturbances. The presence of noise and dynamic working conditions in industrial factories renders traditional speech recognition systems susceptible to a range of challenges, including real-time algorithms, antidisturbance measures, versatility, accuracy, and other factors. To address these challenges, this article proposes the construction of a real-time system for speech recognition of digital humans with high perturbation resistance. The proposed system integrates a speech endpoint detection model, a speech denoising model, a speech-text recognition model, and a voiceprint recognition model. Additionally, a target speech segment duration algorithm based on Gaussian mixture model (GMM) is introduced to enhance the real time and robustness of digital human speech recognition. A speech-to-speech deep learning model employing conditional variational autoencoder (VAE) and adversarial training methods is proposed to address the significant decline in system recognition rate in low signal-to-noise ratio (SNR) environments. The experimental findings, obtained in a mixed industrial continuous and impulse noise environment, demonstrate that the enhanced digital human speech recognition system can elevate the correct text recognition rate and the correct identity recognition rate by up to 65.23% and 61%, respectively, in a high-noise industrial environment. This observation signifies that the industrial digital human speech system delineated in this article possesses the capability and worth to be implemented in industrial environments, thereby offering a valuable solution.