Conference Speech Translation Using Video-Guided Speaker Segmentation
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Solution Overview
Problem
Existing multilingual speech recognition and translation technologies face challenges in accurately handling homophones and multi-speaker environments, leading to reduced recognition and translation accuracy.
Innovation Solution
The method employs video data analysis to determine attendee states, using a recognition model to segment audio data based on language family recognition results, thereby improving speech recognition accuracy by mitigating homophone confusion and grouping speaker features in multi-speaker scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional speech recognition technology is used to convert speeches to text, then the translation process can proceed, but homophones and multi-speaker environments cause reduced recognition accuracy
Solution Approach 1:
The audio data is segmented into multiple audio segments based on video recognition results (detecting number of attendees, body movements, facial movements) and language family recognition results. This segmentation allows the system to process speech from different speakers separately, resolving the multi-speaker environment problem and improving recognition accuracy by preventing speech overlap confusion.
Solution Approach 2:
Video recognition results serve as an intermediary to bridge audio data and speech recognition processing. The video analysis (detecting attendee presence, body movements, facial movements) provides contextual information that helps accurately segment and attribute audio segments to specific speakers, thereby resolving homophone and multi-speaker ambiguities before text conversion.
2Reliability
If audio data is processed without video analysis, then the processing speed is faster, but the ability to handle multi-speaker environments and homophones is insufficient
Solution Approach 1:
The video recognition module performs multiple functions simultaneously: detecting the number of attendees, analyzing body movements, and detecting facial movements. This multi-functionality allows a single video analysis component to provide comprehensive contextual information for speaker segmentation and identification, improving reliability without proportionally increasing system complexity.
Solution Approach 2:
The system merges video recognition results with language family recognition results to guide audio segmentation and speech recognition. By combining these different types of recognition results, the system creates a unified processing framework that handles multi-speaker environments and homophones effectively, improving reliability while managing complexity through integrated processing.
Data Source
AI summary
The present invention provides a multilingual speech recognition and translation method for a conference. The conference includes at least one attendee, and the method includes: receiving, at a server, at least one piece of audio data and at least one piece of video data generated by at least one terminal apparatus; analyzing the video data to generate a video recognition result related to an attendance, and an ethnic of the attendee and a body movement, and a facial movement of the attendee when talking; generating at least one language family recognition result according to the video recognition result and the audio data, and obtaining a plurality of audio segments corresponding to the attendee; performing speech recognition on and translating the audio segments; and displaying a translation result on the terminal apparatus. The method further determines a quantity of conference attendees according to their respective distances from their device microphones.