Multilingual Acoustic Model Construction via Phoneme Correlation Removal
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Solution Overview
Problem
Existing multilingual acoustic models face challenges in covering diverse linguistic characteristics across regions, requiring separate models for each language and needing large datasets, which is impractical for languages with limited data.
Innovation Solution
A method that divides input features into common and distinctive language portions, using a neural network to estimate and remove phoneme correlations, and constructs a Subspace Gaussian Mixture Model (SGMM) acoustic model by adapting parameters from abundant language data to support voice recognition in languages with limited data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single acoustic model is used for all languages, then device complexity is reduced, but voice recognition accuracy deteriorates due to inability to cover diverse linguistic characteristics
Solution Approach 1:
The acoustic model is segmented into language-specific components, allowing different acoustic models to be used for different languages while maintaining a unified framework. This segmentation enables the system to adapt to diverse linguistic characteristics without requiring a completely separate system for each language.
Solution Approach 2:
The patent creates a universal acoustic model framework that can function across multiple languages by incorporating language identification and adaptive selection mechanisms. The system maintains multi-functionality by being able to switch between different language models or adapt a single model to handle various linguistic characteristics.
2Reliability
If separate acoustic models are constructed for each language, then voice recognition accuracy is improved, but device complexity increases and data acquisition becomes difficult for languages with limited resources
Solution Approach 1:
The patent merges multiple language-specific acoustic models into a unified framework by introducing language identification modules and adaptive selection mechanisms. This combining approach allows the system to maintain language-specific accuracy while presenting a unified interface and reducing overall system complexity.
Solution Approach 2:
Language identification modules and adaptive selection mechanisms serve as intermediaries between the user input and the acoustic models. These intermediaries automatically select or adapt the appropriate language model based on the detected language, eliminating the need for users to manually configure language-specific models.
3Quantity of substance
If HMM/GMM-based adaptation technology is used to construct multilingual acoustic models, then data efficiency is improved for languages with limited data, but the models are restricted to languages using the same phoneme-level units
Solution Approach 1:
The patent changes the parameter representation from fixed phoneme-level units to language-adaptive parameters that can accommodate different phoneme systems. By modifying how acoustic parameters are defined and adapted, the system can handle languages with different phonetic characteristics while still benefiting from data efficiency through adaptation technology.
Solution Approach 2:
The acoustic model parameters are made dynamic and adaptable rather than static and fixed. The system can dynamically adjust parameters based on the target language characteristics, allowing the same base model to be adapted to various languages with different phoneme structures through learned parameter transformations.
4Reliability
If large amounts of data are collected for each target language, then acoustic model accuracy is improved, but data acquisition time and cost increase significantly
Solution Approach 1:
The patent performs preliminary actions by pre-training acoustic models on large datasets from languages with abundant data. These pre-trained models serve as a foundation that can be quickly adapted to target languages with limited data, eliminating the need to collect and process large amounts of data for each target language from scratch.
Solution Approach 2:
The system creates copies of acoustic models trained on data-rich languages and adapts them to target languages through parameter adjustment and fine-tuning. Instead of building models from scratch for each language, the patent copies proven models and modifies them to suit the specific characteristics of target languages, significantly reducing data acquisition requirements.
Data Source
AI summary
An apparatus and a method for constructing a multilingual acoustic model, and a computer readable recording medium are provided. The method for constructing a multilingual acoustic model includes dividing an input feature into a common language portion and a distinctive language portion, acquiring a tandem feature by training the divided common language portion and distinctive language portion using a neural network to estimate and remove correlation between phonemes, dividing parameters of an initial acoustic model constructed using the tandem feature into common language parameters and distinctive language parameters, adapting the common language parameters using data of a training language, adapting the distinctive language parameters using data of a target language, and constructing an acoustic model for the target language using the adapted common language parameters and the adapted distinctive language parameters.