Design and Application Research of Embedded Voice Teaching System Based on Cloud Computing

Deep changes are occurring in the components and forms of education as a result ongoing integration development emerging technologies like cloud computing, mobile artificial intelligence with teaching learning, digital transformation is consistently being pushed to new heights. Simultaneously, China’s higher has concurrently reached stage popularization. The digitalization related quality value proposition determines whether it can adapt needs diversification, lifelong training personalization, governance modernization popularization stage. As result, current future phases reform call for accelerating pace education’s guiding high-quality growth innovation. application potential intelligent learning systems becoming more clear this context. In view this, work draws from previous research experiences build implement an embedded voice system based on computing deep model meet education. On one hand, well compensate flaws shortcomings means universities realize accompanying ubiquitous by relying powerful storage capacity platform. other study designs set recognition methods integrating HMM + LSTM enhance system’s performance, ultimately allowing feature be implemented pedagogical system. When comes processing audio signals, hybrid makes use both HMM’s robust time capability neural network’s characterization generalization performance. rate, anti-interference noise robustness all significantly improved. Finally, put through its paces experimental setting gauge performance functionality. results tests demonstrate that created high accuracy good immunity, which will utilized foundation design final Meanwhile, functional modules have achieved expected stability reliability. Trial gathered interviews questionnaires enhances adaptability college conducive promoting improvement students’ cultural literacy innovation ability.