Multilingual Speech Recognition With Selective Output Layers
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional speech recognition systems struggle with supporting multiple languages, requiring separate models for each language, leading to increased memory usage and lower recognition rates, and often necessitate manual language selection by users.
Innovation Solution
A method and apparatus for multilingual speech recognition using a single artificial intelligence model with a speech language classifier and output layer selector, activating specific output layers based on language classification, and employing a general-purpose layer for low confidence scenarios to prevent malfunctions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate models are used for each language, then language recognition accuracy is improved, but memory usage increases
Solution Approach 1:
The patent merges multiple language models into a single unified model that processes multiple languages simultaneously. The model architecture integrates shared layers for acoustic features and language-specific projection layers, allowing one model to handle multiple languages without requiring separate dedicated models for each language, thus reducing overall memory usage while maintaining recognition accuracy.
Solution Approach 2:
The unified speech recognition model is designed with multi-functionality to handle multiple languages through a single model instance. The model includes universal acoustic modeling layers that are language-agnostic and language-specific projection output layers that adapt to different languages, enabling one model to serve multiple language recognition functions without requiring separate specialized models.
2Adaptability or versatility
If multiple language models are deployed, then multilingual support is improved, but device complexity increases
Solution Approach 1:
The patent combines multiple language-specific models into a single unified model architecture. Instead of deploying separate models for each language, the system uses one integrated model that shares common acoustic processing layers and includes language-specific projection layers, thereby reducing system complexity while maintaining comprehensive multilingual support.
Solution Approach 2:
The unified model is segmented into distinct functional components: shared acoustic feature extraction layers that are common across all languages and language-specific projection output layers that handle language discrimination. This segmentation allows the model to maintain multilingual capability while organizing complexity in a manageable, modular fashion that simplifies deployment and processing.
3Quantity of substance
If a single model is used for multiple languages, then memory usage is reduced, but recognition rate deteriorates
Solution Approach 1:
The unified model applies local quality by having different parts of the model serve different functions with specialized characteristics. The shared acoustic layers provide general-purpose feature extraction optimized for all languages, while the language-specific projection layers provide specialized output for each language. This local specialization within the unified architecture maintains high recognition rates while using a single model.
4Device complexity
If manual language selection is required, then system simplicity is maintained, but ease of operation deteriorates
Solution Approach 1:
The system implements self-service by automatically detecting the language of the input audio and selecting the appropriate language model or adjusting the unified model's output layers accordingly. This automatic language detection and selection eliminates the need for manual language input from users, maintaining system simplicity while significantly improving ease of operation.
Solution Approach 2:
The system performs preliminary action by pre-configuring the unified model with multiple language projection layers and pre-processing the audio to identify language characteristics. This preliminary setup and language identification allows the system to automatically select the appropriate processing path without requiring users to manually specify their language preference, thereby improving user convenience while keeping the system relatively simple.
Data Source
AI summary
A method for automatic multilingual speech recognition may comprise: recognizing input audio data by a speech recognizer; classifying the audio data by a speech language classifier; activating, by an output layer selector coupled to the speech recognizer, any one projection output layer of a plurality projection output layers respectively connected to the speech recognizer according to language classification information received from the speech language classifier, an output unit of the activated projection output layer being configured as several bytes; and recombining outputs output in a unit of the several bytes by the activated projection output layer, and outputting the recombined output as an automatic speech recognition result for the audio data.