End-to-End Speech Recognition System Merging Acoustic and Language Models
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Solution Overview
Problem
Current automatic speech recognition (ASR) systems face challenges in achieving accurate and adaptive speech recognition across multiple languages, particularly when languages are vastly different, such as English and Mandarin, due to variability in speech and acoustics, requiring complex component tuning and limited generalization across environments.
Innovation Solution
The development of an end-to-end deep learning system, Deep Speech 2 (DS2), which utilizes a combination of Connectionist Temporal Classification (CTC) loss function, Batch Normalization, and curriculum learning strategies, along with large labeled training datasets and scalable GPU training, to create a unified architecture capable of recognizing multiple languages with high accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional ASR systems use separate components for acoustic modeling, language modeling, and decoding, then each component can be optimized independently, but the overall system complexity increases and generalization across multiple languages deteriorates
Solution Approach 1:
The patent merges acoustic modeling, language modeling, and decoding into a single unified end-to-end neural network model. This consolidation eliminates the need for separate components and their individual optimization, reducing system complexity while maintaining or improving recognition accuracy through joint training and unified architecture.
Solution Approach 2:
The unified neural network model is designed to handle multiple languages and speech variations through a single architecture. By using shared parameters and end-to-end training, the system achieves multi-functionality across different languages and acoustic conditions without requiring separate component tuning for each language pair.
2Measurement precision
If ASR systems are trained with extensive component tuning for multiple languages, then language-specific accuracy improves, but training time and computational resources increase significantly
Solution Approach 1:
The system performs preliminary action by pre-training a unified neural network model on large-scale multi-language data before deployment. This pre-training establishes a strong foundation that can be quickly adapted to specific language pairs without extensive fine-tuning, significantly reducing the time required for language-specific optimization while maintaining high accuracy.
Solution Approach 2:
The patent employs parameter sharing and dynamic parameter adjustment across different languages within the unified model. By changing only specific language-related parameters while keeping the core architecture fixed, the system achieves language-specific accuracy without requiring complete retraining, thus reducing computational time and resources.
3Reliability
If ASR systems use complex component-based architectures, then individual components can be optimized, but adaptability to new languages and environments deteriorates
Solution Approach 1:
The unified neural network model is designed with universal features that enable it to adapt to new languages and acoustic environments without requiring separate component optimization. The end-to-end architecture learns language-invariant representations that can be applied across different languages, improving adaptability while maintaining reliability through the unified training framework.
Data Source
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AI summary
Embodiments of end-to-end deep learning systems and methods are disclosed to recognize speech of vastly different languages, such as English or Mandarin Chinese. In embodiments, the entire pipelines of hand-engineered components are replaced with neural networks, and the end-to-end learning allows handling a diverse variety of speech including noisy environments, accents, and different languages. Using a trained embodiment and an embodiment of a batch dispatch technique with GPUs in a data center, an end-to-end deep learning system can be inexpensively deployed in an online setting, delivering low latency when serving users at scale.