Speaker Recognition for Automatic Language Translation
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Solution Overview
Problem
Conventional translation devices require manual operation to switch languages each time speakers change, leading to inefficiencies and disruptions in conversations involving multiple languages.
Innovation Solution
An information processing method that sets language information for speakers before a conversation, generates speaker models, and automatically recognizes speakers to perform seamless language translation without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual operation is used to switch translation languages for each speaker, then translation accuracy can be controlled, but conversation efficiency deteriorates due to frequent operations
Solution Approach 1:
The translation device automatically detects speaker changes and switches translation languages without user intervention. The device serves itself by using speaker recognition technology to identify when a different speaker is talking and automatically adjusts the translation direction, eliminating the need for manual operations while maintaining accurate translation.
Solution Approach 2:
The manual mechanical operation of pressing buttons to switch languages is replaced with an automated speaker recognition system. The system uses acoustic signal processing and pattern recognition to detect speaker changes and automatically controls the translation language switching, substituting human mechanical interaction with an automated technical system.
2Ease of operation
If automated speaker recognition is implemented, then conversation smoothness improves, but device complexity increases
Solution Approach 1:
The translation device integrates multiple functions into a single system: it performs both translation and speaker recognition automatically. The device universally handles language translation in both directions (first language to second language and vice versa) while simultaneously detecting speaker changes, reducing the need for separate dedicated devices for each function.
3Measurement precision
If speaker models are generated and stored, then speaker recognition accuracy improves, but memory requirements increase
Solution Approach 1:
The system generates and stores speaker models locally for each identified speaker. Each speaker model is stored in the memory with associated speaker information, allowing the device to recognize and differentiate between multiple speakers. The memory stores structured data including speaker identifiers and their corresponding language preferences for accurate translation.
Data Source
AI summary
This information processing method includes: acquiring a first speech signal including a first utterance; acquiring a second speech signal including a second utterance; recognizing whether the speaker of the second utterance is a first speaker by comparing a feature value for the second utterance and a first speaker model; when the first speaker is recognized, performing speech recognition in a first language on the second utterance, generating text in the first language corresponding to the second utterance subjected to speech recognition in the first language, and translating the text in the first language into a second language; and, in a case where the first speaker is not recognized, performing speech recognition in the second language on the second utterance, generating text in the second language corresponding to the second utterance subjected to speech recognition in the second language, and translating the text in the second language into the first language.