Unsupervised ASR Model Generation via Output Distribution Matching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current automatic speech recognition (ASR) systems rely on supervised deep learning, requiring large amounts of human-labeled data for training, which is time-intensive, error-prone, and inefficient, especially for low-resource languages where data acquisition is difficult or impossible.
Innovation Solution
The method generates an ASR model using unsupervised learning and an output distribution matching (ODM) technique, which determines phoneme sequences and boundaries in speech waveforms without human-labeled data, allowing for improved accuracy and efficiency in model generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised deep learning is used for ASR model training, then recognition accuracy can be improved, but large amounts of human-labeled data are required which increases time consumption and manual effort
Solution Approach 1:
The system performs self-labeling by automatically generating phoneme sequences and boundaries from unlabeled speech waveforms using acoustic models and sequence-to-sequence architectures, eliminating the need for manual human labeling while maintaining training data quality
Solution Approach 2:
The system pre-processes unlabeled speech data to generate phoneme annotations and boundaries before actual model training, preparing training data in advance through automatic transcription and alignment processes that would otherwise require manual intervention
2Reliability
If supervised learning with human-labeled data is used, then model training reliability is improved, but the process becomes error-prone and inefficient due to manual labeling
Solution Approach 1:
The system automatically generates training data with phoneme annotations through self-service mechanisms, using acoustic models and sequence-to-sequence architectures to produce reliable labeled data without human intervention, thereby maintaining reliability while eliminating manual errors and improving efficiency
Solution Approach 2:
The system employs feedback loops where the ASR model predictions are compared against generated phoneme sequences, and the discrepancies are used to refine both the model and the labeling process, continuously improving reliability through iterative optimization
3Measurement precision
If extensive human-labeled training data is collected, then ASR model accuracy is improved, but data acquisition becomes difficult or impossible for low-resource languages
Solution Approach 1:
The system enables self-supervised learning where the model learns from unlabeled speech data by automatically generating its own training labels through acoustic modeling and sequence-to-sequence transcription, making data acquisition feasible for low-resource languages without requiring manual labeling infrastructure
Solution Approach 2:
The system uses universal acoustic models and sequence-to-sequence architectures that can process any language's speech waveforms, enabling the same framework to generate training data for multiple languages including low-resource ones, thereby making the solution universally applicable across different language resources
Data Source
AI summary
A method for generating an automatic speech recognition (ASR) model using unsupervised learning includes obtaining, by a device, text information. The method includes determining, by the device, a set of phoneme sequences associated with the text information. The method includes obtaining, by the device, speech waveform data. The method includes determining, by the device, a set of phoneme boundaries associated with the speech waveform data. The method includes generating, by the device, the ASR model using an output distribution matching (ODM) technique based on determining the set of phoneme sequences associated with the text information and based on determining the set of phoneme boundaries associated with the speech waveform data.