Communication Session Quality Assessment via Audio Text Conversion
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Solution Overview
Problem
Assessing the quality of communication sessions in a 1-to-many mode, such as online lectures, is challenging due to the diversity of listeners, content, speakers, hardware, and software platforms involved, leading to a need for a holistic assessment of semantic information transmission.
Innovation Solution
An apparatus and method for assessing communication session quality by continuously receiving and converting audio streams into text data, determining quality features like understandability, information quality, and attention, and providing feedback to improve user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple quality features are monitored to provide holistic assessment, then assessment comprehensiveness is improved, but system complexity increases
Solution Approach 1:
The quality assessment system is divided into multiple independent quality feature modules (articulation quality, grammar quality, information quality, attention quality, feedback quality) that can be processed separately and then integrated. Each module focuses on a specific aspect of communication quality, making the overall complex system manageable through modular decomposition.
Solution Approach 2:
The system employs a multi-functional apparatus that simultaneously performs speech-to-text conversion, quality feature extraction, audio stream analysis, and feedback generation. This universal system handles diverse quality assessment tasks through integrated processing of audio streams and text data across multiple quality dimensions.
2Ease of operation
If real-time monitoring and feedback are implemented, then user experience improvement is enhanced, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary speech-to-text conversion and quality feature extraction during the communication session itself, rather than analyzing recordings afterward. By preparing and analyzing data in real-time as it is generated, the system enables immediate feedback without significant post-processing delays.
Solution Approach 2:
The system implements continuous feedback loops where quality features are monitored in real-time and feedback is provided to users during the communication session. This allows speakers to immediately adjust their delivery based on articulation, grammar, information quality, and attention metrics, improving user experience through timely interventions.
Data Source
AI summary
Apparatus (5) for assessing a quality of a communication session (3) between at least one first party (1) and at least one second party (2a, 2b . . . 2N), over a telecommunication network (4), comprising means for:monitoring (S1) said communication session by continuously receiving an audio stream associated with said communication session;converting (S2) language of said audio stream into text data;determining (S3), from said text data, at least first understandability quality features (UQFA, UQFG) and an information quality feature (IQF), said first understandability quality feature being representative of at least word articulation and grammar correctness within said language, and said information quality feature being representative of a comparison of the semantic content of said audio stream with a set of contents related to said audio stream;assessing (S4) said quality from said quality features.