Multilingual Speech Recognition via Language Segmentation and Intent Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current speech recognition systems face challenges in accurately recognizing and understanding speech that includes multiple languages, leading to low recognition rates and difficulty in detecting the intention behind such speech commands.
Innovation Solution
An AI apparatus and method that can recognize speech including multiple languages by detecting the main language and analyzing the intention within it, reducing the failure possibility of intention analysis by using a combination of language detection, translation, and keyword extraction techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a speech recognition model processes speech to be appropriate to each language while distinguishing between languages, then language-specific recognition accuracy is improved, but recognition rate for sentences including multiple languages deteriorates
Solution Approach 1:
The speech processing system is segmented into multiple language-specific recognition models, each specialized for a particular language. The system divides the multilingual speech input into language segments and processes each segment through the corresponding language-specific model, thereby maintaining high accuracy for each language while successfully handling mixed-language inputs.
Solution Approach 2:
A language detection module serves as an intermediary between the multilingual speech input and the language-specific recognition models. This mediator identifies the language type of each speech segment and routes it to the appropriate recognition model, enabling seamless processing of multiple languages without compromising recognition accuracy.
2Adaptability or versatility
If speech command includes multiple languages, then communication versatility is improved, but ability to detect the intention of the speech command deteriorates
Solution Approach 1:
The intention detection process is segmented into language identification and intention analysis stages. The system first segments the multilingual speech by language type, processes each segment through appropriate language models, and then integrates the results to detect the overall user intention, making the detection process manageable despite language diversity.
Solution Approach 2:
The speech recognition system is designed with universal intention detection capability that works across multiple languages. By implementing a unified intention detection framework that can handle various language inputs through standardized processing steps, the system maintains both communication versatility and effective intention detection.
Data Source
AI summary
An AI apparatus includes a microphone to acquire speech data including multiple languages, and a processor to acquire text data corresponding to the speech data, determine a main language from languages included in the text data, acquire a translated text data obtained by translating a text data portion, which has a language other than the main language, in the main language, acquire a morpheme analysis result for the translated text data, extract a keyword for intention analysis from the morpheme analysis result, acquire an intention pattern matched to the keyword, and perform an operation corresponding to the intention pattern.