Acoustic Model Adaptation for Low-Resource Languages
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Solution Overview
Problem
Existing speech recognition systems face challenges in generating accurate acoustic models for languages or dialects with limited training data, as they require diverse and ample transcribed audio data for effective transcription, which is often not available.
Innovation Solution
The method involves generating cross-domain or cross-dialect acoustic models by transforming and adapting source language models to target languages using techniques like Linear Discriminant Analysis (LDA), Vocal Tract Length Normalization (VTLN), and Constrained Maximum Likelihood Linear Regression (CMLLR), allowing for the use of existing models from more robust languages to improve transcription accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If acoustic models are generated using traditional methods with ample transcribed audio data, then transcription accuracy is improved, but the method cannot be applied to languages or dialects with limited training data
Solution Approach 1:
The patent uses a source language acoustic model as an intermediary to bridge the gap between limited target language data and accurate transcription. The source model, trained on abundant data, serves as a mediator that provides initial acoustic knowledge which is then adapted to the target language through feature space transformation and model adaptation techniques, enabling accurate transcription despite limited target language training data
Solution Approach 2:
The patent transforms acoustic features from the source language feature space to the target language feature space using parameter changes. By applying transformations such as VTLN (Vocal Tract Length Normalization) and other feature space mappings, the acoustic characteristics are adjusted to match the target language, allowing the model to achieve accurate transcription performance with limited target language data
2Measurement precision
If acoustic models are trained separately for each language or dialect, then language-specific accuracy is improved, but the complexity and resource requirements increase significantly
Solution Approach 1:
The patent creates a universal source language acoustic model that can serve multiple target languages and dialects through adaptation. Instead of training separate models for each language, a single source model is developed and then adapted to various target languages by transforming its acoustic features, reducing the total number of models needed while maintaining language-specific accuracy
Solution Approach 2:
The patent separates the acoustic modeling process into two distinct stages: (1) training a general source language model on abundant data, and (2) adapting this model to specific target languages through feature space transformation. This segmentation allows the complex task of creating language-specific models to be broken down into a reusable base model creation step and lighter-weight adaptation steps for each target language
Data Source
AI summary
This document describes methods, systems, techniques, and computer program products for generating and/or modifying acoustic models. Acoustic models and/or transformations for a target language/dialect can be generated and/or modified using acoustic models and/or transformations from a source language/dialect.