Live Conversation Transcription With Speaker Segmentation
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Solution Overview
Problem
Conventional methods for capturing and recording conversations are inefficient and prone to human error, leading to inaccurate information extraction and distraction from the conversation.
Innovation Solution
A system and method for capturing, processing, and rendering context-aware moment-associating elements, such as conversations, by segmenting audio-form conversations, assigning speaker labels, and generating synchronized transcriptions for live broadcasting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional note-taking is performed during a conversation, then information can be recorded, but the note-taker becomes distracted from the conversation and accuracy decreases due to human error
Solution Approach 1:
The patent replaces the mechanical human note-taking process with an automated speech recognition system that uses audio processing and natural language processing algorithms to capture and transcribe conversation content, eliminating human distraction while maintaining or improving accuracy
Solution Approach 2:
The system enables self-service by automatically processing and transcribing conversations without requiring human intervention for note-taking, allowing participants to fully engage in the conversation while the system independently handles information extraction
2Measurement precision
If automated speech recognition is implemented for real-time transcription, then information extraction accuracy improves, but system complexity and processing requirements increase
Solution Approach 1:
The patent segments the speech processing task into distinct modules including audio pre-processing, feature extraction, acoustic modeling, language modeling, and text decoding, allowing each component to be optimized independently and managed separately to reduce overall system complexity
Solution Approach 2:
The system introduces intermediary components such as acoustic models and language models that act as mediators between raw audio input and final transcription output, breaking down the complex recognition task into manageable stages with intermediate representations
3Loss of time
If real-time processing is performed during live broadcasting, then information is available immediately, but processing speed and computational resources are challenged
Solution Approach 1:
The system performs preliminary actions by pre-processing audio data and preparing computational models before actual transcription is needed, allowing real-time processing to operate on pre-prepared data structures and reducing computational latency during live broadcasting
Solution Approach 2:
The patent implements periodic processing where transcription is updated at regular intervals or when specific triggers occur, balancing the need for real-time information availability with computational resource constraints by processing in discrete cycles rather than continuous operation
Data Source
AI summary
Computer-implemented method and system for processing and broadcasting one or more moment-associating elements. For example, the computer-implemented method includes granting subscription permission to one or more subscribers; receiving the one or more moment-associating elements; transforming the one or more moment-associating elements into one or more pieces of moment-associating information; and transmitting at least one piece of the one or more pieces of moment-associating information to the one or more subscribers. In certain examples, transforming the one or more moment-associating elements includes: segmenting the one or more moment-associating elements into a plurality of moment-associating segments; assigning a segment speaker for each segment of the plurality of moment-associating segments; transcribing the plurality of moment-associating segments into a plurality of transcribed segments; and generating the one or more pieces of moment-associating information based the plurality of transcribed segments and the segment speaker assigned for each segment of the plurality of moment-associating segments.