Speech recognition technology, a pivotal element in human-computer interaction, has witnessed substantial advancements in recent years, propelled by the synergies of deep learning and big data. This paper provides a systematic review of the evolution of speech recognition algorithms, delineating the principal characteristics and application contexts of traditional speech recognition algorithms, such as Hidden Markov Models (HMM), deep learning-based algorithms, including Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN), and end-to-end speech recognition algorithms. Furthermore, this study delves into the multifaceted applications of these algorithms in domains such as voice assistants (e.g., Siri and Alexa), machine translation, and meeting transcription, elucidating their transformative impact. The paper also synthesizes the prevailing speech recognition technologies and the challenges they confront, with a particular emphasis on the limitations of commonly used language recognition algorithms, such as susceptibility to noise, accent variability, and data dependency. Through this comprehensive analysis, the paper aims to illuminate the current state and future trajectories of speech recognition technology. This paper identifies and summarizes the shortcomings of commonly used language recognition algorithms.
Xuyang Chen (Wed,) studied this question.