Multilingual Speech Recognition via Dynamic Engine Switching
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Solution Overview
Problem
Current speech recognition systems face challenges in transcribing multi-lingual conversations as they typically support only one language domain, requiring manual language specification and struggling to adapt to audio streams with alternating languages.
Innovation Solution
The system dynamically switches between different speech recognition engines based on real-time language identification, generating and replacing transcriptions in the transcription cache to accurately transcribe spoken language utterances in multiple languages, ensuring that each portion of the audio stream is transcribed by the appropriate engine.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single speech recognition engine is used, then the system is simple to operate, but it cannot accurately transcribe multi-lingual audio streams
Solution Approach 1:
The patent segments the speech recognition system into multiple specialized engines, each optimized for a specific language. A language identification module divides the multi-lingual audio stream into language-specific segments, routing each segment to the appropriate speech recognition engine. This segmentation enables accurate transcription of multi-lingual content while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The patent implements a universal speech recognition system that can handle multiple languages through a single integrated architecture. The language identification module acts as a universal component that recognizes various languages and dynamically routes audio segments to the appropriate specialized engines. This multi-functional approach allows the system to process diverse language inputs without requiring separate standalone systems for each language.
2Measurement precision
If manual language specification is required, then the speech recognition engine can be configured correctly, but the ease of operation is reduced
Solution Approach 1:
The patent implements a self-service language identification system where the language identification module automatically detects the language of audio segments without requiring user intervention. The system autonomously configures itself by routing detected language segments to the appropriate speech recognition engines, eliminating the need for manual language specification while maintaining high detection accuracy through automated language recognition algorithms.
Solution Approach 2:
The patent employs feedback mechanisms where the language identification module continuously analyzes audio input, detects language changes, and dynamically adjusts the routing to appropriate speech recognition engines. This real-time feedback loop enables the system to adapt to changing language conditions automatically, providing accurate language detection and configuration without requiring manual user input or reconfiguration.
3Measurement precision
If speech recognition systems are trained for a single user and single environment, then the transcription accuracy is high, but the adaptability to different languages is limited
Solution Approach 1:
The patent segments the speech recognition capability into multiple specialized engines, each trained for a specific language. This allows each engine to maintain high transcription accuracy for its designated language while the overall system gains multi-lingual versatility. The language identification module segments the audio stream and routes each segment to the appropriately trained engine, preserving the benefits of specialized training across multiple languages.
Solution Approach 2:
The patent merges multiple single-language speech recognition engines into a unified multi-lingual system. By combining several specialized engines, each with high accuracy for their respective languages, the system achieves both high transcription accuracy and broad multi-lingual adaptability. The language identification module coordinates the merged engines to process different language segments, creating a versatile system that maintains the accuracy benefits of specialized training.
Data Source
AI summary
Systems are provided for multilingual speech data processing. A language identification module is configured to analyze spoken utterances in an audio stream and to detect at least one language corresponding to the spoken language utterances. The language identification module detects that a first language corresponds to the first portion of the audio stream. A first transcription of the first portion of the audio stream in the first language is generated and stored in a cache. A second transcription of a second portion of the audio stream in the first language is also generated and stored. When the second portion of the audio stream corresponds to a second language, a third transcription is generated in the second language using a second speech recognition engine configured to transcribe spoken language utterances in the second language. Then, the second transcription is replaced with the third transcription in the cache and any displayed instances.