Nuance Dragon Professional as a Cloud Solution for Effective Speech-to-Text Conversion
Keywords:
Speech-to-text, Cloud solution, Nuance Dragon Professional, Transcription accuracy, Workflow optimizationAbstract
The cloud integration of Nuance Dragon Professional improves speech-to-text conversion for professionals in numerous sectors, ensuring smooth and accurate transcription. Cloud computing is used to provide real-time, scalable, secure transcribing services that adapt to customer demands. This method addresses the problems of classic voice recognition systems by providing excellent accuracy in multiple language situations, even with background noise or accents. Cloud architecture allows users to access the system from any internet-connected device, providing ease and flexibility. The objective is to produce an efficient and trustworthy tool that can boost productivity in healthcare, legal, and customer service, where precise transcribing is crucial. It also reduces manual data input to improve productivity and user happiness. From Speech_Processing_Metrics dataset, 5 samples with 5 parameters are analyzed. Processing Time (s) ranges from 1.04 to 2.45, Transcription Accuracy (%) is 87.23 to 95.86, Word Error Rate (%) is 1.45 to 4.58, Latency (ms) is 69 to 180, and Clarity Score (1-10) is 7 to 9. Studying Noise_Filtering_Effectiveness dataset for 5 audios with 5 parameters. Noise Reduction (%) is 83.41–89.4, Clarity Improvement (%) is 15.32–29.69, Processing Time (ms) is 52–137, Post-Filter Clarity Score (1-10) is 8, 9, Word Recognition Improvement (%) is 5.44–15.95. Five applications with five parameters are analyzed from Application_Integration dataset. The Usage Frequency (%) ranges from 23.26 to 69.53, Integration Latency (ms) from 84 to 165, System Compatibility (%) from 90.56 to 98.6, Error Rate (%) from 0.45 to 1.89, and Uptime (%) from 98.32 to 99.8.
References
[1]. F. Battaglia, “From ‘Listen and Repeat’ to ‘Listen and Revise’: How to Transcribe Interviews Offline Quickly and for Free Using Voice Recognition Software,” International Journal of Qualitative Methods, vol. 23, pp. 1-15, 2024.
[2]. D. Y. Cao, J. R. Silkey, M. C. Decker and K. A. Wanat, “Artificial Intelligence-Driven Digital Scribes in Clinical Documentation: Pilot Study Assessing the Impact on Dermatologist Workflow and Patient Encounters,” JAAD International, vol. 15, pp. 149-151, 2024.
[3]. K. Crawford, Y. X. Khoo, A. Kumar, H. Mentis, and F. Hamidi, “Decoding the Privacy Policies of Assistive Technologies,” in Proceedings of the 21st International Web for All Conference, pp. 87-95, 2024.
[4]. S. R. Thumala and B. S. Pillai, “Cloud cost optimization methodologies for cloud migrations,” International Journal of Intelligent Systems and Applications in Engineering, vol. 12, no. 2, pp. 4797–4809, 2024.
[5]. S. N. Ghanta, S. J. Al’Aref, A. Lala-Trinidade, G. N. Nadkarni, S. Ganatra, S. S. Dani, and J. L. Mehta, “Applications of ChatGPT in Heart Failure Prevention, Diagnosis, Management, and Research: A Narrative Review,” Diagnostics, vol. 14, no. 21, pp. 1-18, 2024.
[6]. V. Ramesh, “Evaluating Apache Kafka performance and operational efficiency: A comparative study of ZooKeeper and KRaft architectures,” International Journal of Computer Applications, vol. 187, no. 46, pp. 12–18, 2025.
[7]. T. Haberle, C. Cleveland, G. L. Snow, C. Barber, N. Stookey, C. Thornock, L. Younger, B. Mullahkhel, and D. Ize-Ludlow, “The Impact of Nuance DAX Ambient Listening AI Documentation: A Cohort Study,” Journal of the American Medical Informatics Association, vol. 31, no. 4, pp. 975-979, 2024
[8]. S. R. Thumala, H. Madathala and S. Sharma, "Towards Sustainable Cloud Computing: Innovations in Energy-Efficient Resource Allocation," International Conference on Machine Learning and Autonomous Systems (ICMLAS), pp. 1528-1533, 2025.
[9]. K. T. Kavanagh, C. Pontus, and L. E. Cormier, “Healthcare Violence and the Potential Promises and Harms of Artificial Intelligence,” Journal of Patient Safety, vol. 20, no. 5, pp. 307-313, 2024.
[10]. J. J. Li, S. Bray, J. Fiorini, and P. Mullins, “A Counseling Student’s Experiences with Vision Impairment: A Narrative Inquiry,” Journal of Counselor Preparation and Supervision, vol. 18, no. 1, pp. 1-15, 2024.
[11]. T. L. Liu, C. Hetherington, C. Stephens, A. McWilliams, A. Dharod, T. Carroll, and J. A. Cleveland, “AI-Powered Clinical Documentation and Clinicians’ Electronic Health Record Experience: A Nonrandomized Clinical Trial,” JAMA Network Open, vol. 7, no. 9, pp. 1-4, 2024
[12]. V. Ramesh, “Performance benefits of reactive frameworks,” International Journal of Computer Applications, vol. 975, pp. 8887, 2025.
[13]. S. R. Thumala, H. Madathala and V. M. Mane, "Azure Versus AWS: A Deep Dive into Cloud Innovation and Strategy," International Conference on Electronics and Renewable Systems (ICEARS), pp. 1047-1054, 2025.
[14]. A. A. Onitilo, A. R. Shour, D. S. Puthoff, Y. Tanimu, A. Joseph, and M. T. Sheehan, “Evaluating the Adoption of Voice Recognition Technology for Real-Time Dictation in a Rural Healthcare System: A Retrospective Analysis of Dragon Medical One,” PLOS ONE, vol. 18, no. 3, pp. 1-17, 2023.
[15]. L. M. Owens, J. J. Wilda, R. Grifka, J. Westendorp, and J. J. Fletcher, “Effect of Ambient Voice Technology, Natural Language Processing, and Artificial Intelligence on the Patient–Physician Relationship,” Applied Clinical Informatics, vol. 15, no. 4, pp. 660-667, 2024.
[16]. H. Madathala, S. R. Thumala, and G. Yeturi, “Optimizing cloud migration: Designing robust architectures for seamless transition from on-premises to Azure for SAP and database systems,” International Journal of Engineering Technology Research & Management, vol. 9, no. 1, 2025.
[17]. A. M. Stoughton and O. Kang, “A Systematic Review of Empirical Mobile-Assisted Pronunciation Studies Through a Perception–Production Lens,” Languages, vol. 9, no. 7, pp. 1-15, 2024
[18]. R. R. Vanam, C. R. Krishnama, S. Elumalai and S. R, "Enhancing Software Reliability through Anomaly Detection: Implementing Variational Autoencoders for Real-time Performance Monitoring and Error Prediction," 6th International Conference for Emerging Technology, pp. 1-8,2025.
[19]. Z. Yang, D. Wang, F. Zhou, D. Song, Y. Zhang, J. Jiang, K. Kong, X. Liu, Y. Qiao, R. T. Chang, and Y. Han, “Understanding Natural Language: Potential Application of Large Language Models to Ophthalmology,” Asia-Pacific Journal of Ophthalmology, vol. 13, no. 4, pp. 1-30, 2024.
[20]. M. M. Yekta, “The General Intelligence of GPT-4, Its Knowledge Diffusive and Societal Influences, and Its Governance,” Meta-Radiology, vol. 2, no. 2, pp. 1-17, 2024
