title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¿ÑÐ¾ÑÐ¾ÑÐ¸Ð¿Ð° ÑÐ¸ÑÑÐµÐ¼Ñ Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð½Ð¾Ð³Ð¾ ÑÐ¾ÑÐ¼Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð¾ÑÑÑÑÐ¾Ð² Ð¾ Ð·Ð²Ð¾Ð½ÐºÐ°Ñ Ð² ÐºÐ¾Ð½ÑÐ°ÐºÑÐ½Ð¾Ð¼ ÑÐµÐ½ÑÑÐµ Ð¾Ð¿ÐµÑÐ°ÑÐ¾ÑÐ° ÑÐ²ÑÐ·Ð¸
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Developing a prototype system of automated call report fill-up for contact center of a telecommunications service provider
Creators: name: ÐÐ°Ð¿ÑÑÑÐºÐ¸Ð½, ÐÐ°ÐºÑÐ¸Ð¼
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2021
Subjects: ÐÐ²ÑÐ¾Ð¼Ð°ÑÐ¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð½ÑÐµ ÑÐ¸ÑÑÐµÐ¼Ñ Ð¿Ð»Ð°Ð½Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð¸ ÑÐ¿ÑÐ°Ð²Ð»ÐµÐ½Ð¸Ñ
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description: The subject of the masterâs degree thesis is âDeveloping a prototype system of automated call report fill-up for contact center of a telecommunications service providerâ. For purpose of master thesis project development, an attempt has been carried out to build a software prototype system enabling automated phone contact report fill-up in telecommunications firm contact center (CC). This software must be able to cover same basic use cases as a full-featured system does which main function make feasible lower need for human involvement into collecting and entering of structured call information into a contact report form. In turn, such automation experience offers prospects to reduce to a minimum the average length of wrap-up intervals which follow agent-caller contacts throughout CC working period. The model building approach has been applied primarily to present a prototype system design. Both customizable ready-to-use components and tools for own code contribution have been picked based on comparative analysis of available open source options. Basic software tests have been performed in the environment of a telecom provider CC to make assessment of common functionality as well as quality scores of system output data. The core development stage combined several tasks: â¢ building and integration of modules that include components to process speech and transcript dataflows; â¢ implementation of both client-server and cross-server interfaces of data exchange within a local network; â¢ training of neural network-based models for automatic text analysis. The prototype software matched basic functional requirements with respect to each relevant use case during tests in the real-world CC of MegaFon, which is an universal telecom provider. Model training yielded comparably solid average precision and recall estimations (85%/89% in classifying along with 88%/91% in named entity recognition task respectively) unless these results are affected by any eventual factor causing deterioration of quality of automatic transcribing. Whenever such effect occurs, there are observations of definitely lower scores pertaining to entity recognition modelling (51%/47%). Taking into account the quality of model outputs, a crucial need is to perform the extended training of acoustic model for speech recognition system, specifically using phone talk records that provide samples of call sound issues like background/channel noise, poor articulation / pronunciation etc. Considerable improvements are achievable in pursuing less RAM consumption, also in leveraging design enhancements to advance latency control and reliability features of network programming.