Speech Recognition Segmentation for Court Transcripts
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Solution Overview
Problem
Conventional speech recognition technologies are inadequate for transcribing free speeches in special circumstances like courts and conferences, as they fail to properly associate modified text with original speeches and are not suited for the unique dialogue structures of these environments, where questions and answers dominate and speakers maintain sequential roles.
Innovation Solution
A speech recognition system that includes a microphone for acquiring speech data, a speech recognition processing unit for segmenting and recognizing speech, a text preparation unit for sorting recognition data by time, and an output control unit for displaying and reproducing text, with additional features like speaker identification and dynamic programming for editing and associating edited text with speech data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional speech recognition technologies are used to transcribe free speeches in courts and conferences, then the transcription process can be automated, but the transcribed text becomes difficult to read and cannot be properly associated with original speeches after modification
Solution Approach 1:
The system segments the transcribed text into units corresponding to individual speakers or speech turns, and associates each segment with the corresponding original speech recording. This segmentation allows the text to be organized in a readable format while maintaining the connection to the original speeches, resolving the contradiction between automated transcription and readability.
Solution Approach 2:
The system introduces an intermediary layer that links modified transcribed text with original speech data through time-based association. This intermediary mechanism allows the text to be edited and modified for readability while preserving the ability to reproduce and associate with the original speeches, solving the contradiction between automation and ease of operation.
2Ease of operation
If the transcribed text is modified to improve readability, then the text becomes easier to read, but the association with original speeches becomes lost
Solution Approach 1:
The system performs preliminary action by establishing time-based associations between transcribed text segments and original speech data before modification occurs. This preliminary linkage ensures that even after the text is modified for readability, the connection to the original speeches is preserved, preventing loss of information.
Solution Approach 2:
The system maintains feedback loops that allow users to navigate between the modified text and the original speech recordings. When text is modified, the system provides feedback mechanisms (such as time stamps or links) that enable users to return to the corresponding original speech segments, ensuring the association is maintained despite modifications.
3Adaptability or versatility
If conventional speech recognition systems are used, then various speech circumstances can be handled, but they are not suitable for special circumstances like courts and conferences with specific dialogue structures
Solution Approach 1:
The system applies local quality by tailoring the transcription and processing methods specifically for court and conference environments. It identifies and handles the unique characteristics of these settings (such as sequential speaker turns, questions and answers, and formal dialogue structures) with specialized processing, improving reliability for these specific applications while maintaining versatility.
Solution Approach 2:
The system incorporates dynamic processing that adapts to the specific dialogue structures of courts and conferences. It dynamically identifies speaker turns, manages sequential dialogue patterns, and adjusts the transcription process to handle the formal nature of these environments, thereby improving reliability for specialized applications.
Data Source
AI summary
An example embodiment of the invention includes a speech recognition processing unit for specifying speech segments for speech data, recognizing a speech in each of the speech segments, and associating a character string of obtained recognition data with the speech data for each speech segment, based on information on a time of the speech, and an output control unit for displaying/outputting the text prepared by sorting the recognition data in each speech segment. Sometimes, the system further includes a text editing unit for editing the prepared text, and a speech correspondence estimation unit for associating a character string in the edited text with the speech data by using a technique of dynamic programming.