Language Model Training via Machine Translation
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Solution Overview
Problem
Conventional language recognition models require extensive human labor and time to train, especially when transitioning between languages, due to the need for specialized skills and the generation of training data, making it cumbersome and inefficient.
Innovation Solution
A language processing engine that utilizes historical and machine-generated data from a reference language to train a target language model, leveraging machine translation and quality checks to reduce human effort and improve accuracy, allowing for the recognition of utterances in multiple languages without requiring continuous human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional language recognition models are trained using manual human effort, then training accuracy can be maintained, but training time and human resources increase significantly
Solution Approach 1:
The system enables models to train themselves by automatically generating training data through machine translation and synthetic utterance generation. The model iteratively improves without human intervention by translating reference utterances to target languages, generating synthetic speech samples, and retraining, creating a self-sustaining training loop that eliminates manual data annotation while maintaining accuracy
Solution Approach 2:
The system performs preliminary actions by pre-translating reference language utterances to target languages and pre-generating synthetic training data before actual model training begins. This preparation phase creates a ready-to-use training dataset that eliminates the need for time-consuming manual annotation during the training process itself
2Adaptability or versatility
If language recognition models are trained for multiple languages, then language versatility improves, but training complexity and resource requirements increase
Solution Approach 1:
The system achieves multi-language capability through a universal training framework that uses machine translation to adapt a single reference language model to multiple target languages. Instead of creating separate training pipelines for each language, the system translates reference utterances into various target languages and uses the same synthetic data generation and model retraining process, making the training system universally applicable to any language pair
Solution Approach 2:
The system introduces machine translation as an intermediary component that bridges the reference language and target languages. Rather than requiring direct human annotation in each target language, the translation service acts as a mediator that converts reference utterances into target language text, which is then converted to synthetic speech for training, simplifying the overall training architecture
3Reliability
If specialized human trainers are used to generate training data, then data quality improves, but operational costs and time consumption increase
Solution Approach 1:
The system replaces the mechanical process of manual human annotation with an automated computational pipeline involving machine translation and synthetic speech generation. Instead of human trainers listening to and transcribing utterances, the system automatically translates reference text to target languages and generates synthetic speech samples, eliminating manual labor while maintaining data quality through controlled generation processes
Data Source
AI summary
Techniques are provided for training a target language model based at least in part on data associated with a reference language model. For example, language data utilized to train an English language model may be translated and provided as training data to train a German language model to recognize utterances provided in German. By utilizing the techniques herein, the efficiency of training a new language model may be improved due at least in part to replacing labor-intensive operations conventionally performed by specialized personnel with machine-generated data. Additionally, techniques discussed herein provide for reducing the time required for training a new language model by leveraging information associated with utterances of one language to train the new language model associated with a different language.