Prototyping Methodology of End‐to‐End Speech Analytics Software - volodymyr-sokolov/publications GitHub Wiki
Conference Paper
Oleh Romanovskyi 
,
Ievgen Iosifov ,
Olena Iosifova ,
Volodymyr Sokolov ,
Pavlo Skladannyi ,
Igor Sukaylo
This paper presents the prototype of end-to-end speech recognition, storage, and postprocessing tasks to build speech analytics, real-time agent augmentation, and other speech-related products. Moving ASR models from the dev environment into production requires both researcher and architectural knowledge, which slows down and limits the possibility of companies benefiting from speech recognition and NLP advances for fundamental business operations. This paper proposes a fast and flexible prototype that can be easily implemented and used to serve ASR/NLP-trained models to solve business problems. Various software solutions’ compatibility problems were solved during the experimental setup assembly, and a working prototype was built and tested. An architectural diagram of the solution was also shown. Performance, limitations, and challenges of implementation are also described.
ASR; Automatic Speech Recognition; Natural Language Processing; NLP; speech analytics
Speech Communication; Neural Network; Language Modeling
2022 Modern Machine Learning Technologies and Data Science Workshop (MoMLeT&DS)
25,26 November 2022 
Leiden, The Netherlands
First Online: 29 December 2022
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ISSN: 1613-0073
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EID: 2-s2.0-85146116195
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KUBG: 43756
Romanovskyi, O., Iosifov, I., Iosifova, O., Sokolov, V., Skladannyi, P., & Sukaylo, I. (2022). Prototyping Methodology of End-to-End Speech Analytics Software. In 4th International Workshop on Modern Machine Learning Technologies and Data Science (Vol. 3312, pp. 76–86).
O. Romanovskyi, I. Iosifov, O. Iosifova, V. Sokolov, P. Skladannyi, and I. Sukaylo, “Prototyping Methodology of End-to-End Speech Analytics Software,” 4th International Workshop on Modern Machine Learning Technologies and Data Science, vol. 3312, pp. 76–86, 2022.
O. Romanovskyi, et al., Prototyping Methodology of End-to-End Speech Analytics Software, in: 4th International Workshop on Modern Machine Learning Technologies and Data Science, vol. 3312 (2022) 76–86.