Real-Time Speaker Identification in Simultaneous Interpretation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing simultaneous interpretation systems struggle with real-time speaker identification, as they require post-recording analysis of speech data to identify speakers, which is not suitable for real-time processing.
Innovation Solution
A simultaneous interpretation device equipped with a speech recognition processing unit, a segment processing unit, a speaker prediction processing unit, and a machine translation processing unit, which performs real-time speech recognition, segment processing, speaker prediction, and machine translation, allowing for simultaneous interpretation with real-time speaker identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If speaker identification is performed by analyzing recorded speech data after recording, then speaker identification accuracy can be achieved, but real-time processing capability is lost
Solution Approach 1:
The system performs preliminary actions by extracting and storing speaker-specific features (voice prints, pitch characteristics, rhythm patterns) during the recording process itself, rather than after. These features are pre-computed and stored in a database, enabling immediate retrieval and matching during real-time processing, thus eliminating the need for post-recording analysis while maintaining identification accuracy
Solution Approach 2:
The invention extracts only the essential speaker identification features from the audio data (such as voice prints, pitch contours, and rhythmic patterns) and separates these from the full speech content. By taking out only the necessary feature components and storing them separately, the system enables rapid retrieval and matching during real-time operation without re-processing the entire recorded data, thus achieving both accuracy and real-time capability
2Measurement precision
If machine translation and speaker identification are performed sequentially, then processing accuracy can be maintained, but processing speed and real-time capability deteriorate
Solution Approach 1:
The system merges the machine translation processing and speaker identification processing into a single parallel execution framework. Both processes receive the same input audio stream and operate simultaneously on different data streams - translation processes the speech content while speaker identification processes the acoustic features. This merging of processing paths enables both high accuracy and real-time performance by eliminating sequential bottlenecks
Solution Approach 2:
The invention segments the processing pipeline into independent functional modules: a speech recognition module that outputs both translated text and speaker feature data, a machine translation module that processes the recognized speech, and a speaker identification module that analyzes acoustic features. These segmented modules can execute in parallel on different data streams, maintaining translation accuracy while significantly improving processing speed through concurrent operations
Data Source
AI summary
Provided is a simultaneous interpretation system that performs machine translation processing and speaker identification processing in real time. In the simultaneous interpretation system, the segment processing unit of the simultaneous interpretation device performs high-speed and highly accurate segment processing to obtain sentence data and also obtains a time range in which a word sequence included in the sentence data was uttered, thus, allowing for performing machine translation processing and speaker identification processing in real time. In other words, in the simultaneous interpretation system, the machine translation processing unit performs machine translation processing on sentence data obtained through high-speed and highly accurate segment processing, and performs, in parallel, processing for predicting a speaker who spoke during the period specified by the time range data based on the inputted video stream and the time range data, thus allowing for performing machine translation processing and speaker identification processing in real time.