Domain-Adaptive Speech Recognition with Phoneme-to-Grapheme Correction
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Solution Overview
Problem
Conventional speech recognition approaches are resource-intensive and error-prone.
Innovation Solution
Implementing a domain adaptive speech recognition system using artificial intelligence, which includes training a first generator to convert phonemes to graphemes, utilizing a biasing language model to refine speech recognition outputs, and performing word replacements to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional speech recognition approaches are used, then speech recognition can be performed, but resource consumption is high and error rate is high
Solution Approach 1:
The speech recognition system is divided into multiple specialized components: phoneme recognition module, grapheme conversion module, and language model module. Each component handles a specific aspect of the recognition process, improving overall accuracy while allowing efficient resource allocation to each segment rather than requiring all resources for a single monolithic system.
Solution Approach 2:
Graphemes are introduced as an intermediary representation between phonemes and final speech recognition output. The system converts phonemes to graphemes using trained conversion models, and then uses language models to refine the recognition based on grapheme sequences. This intermediary layer improves accuracy by providing an additional processing stage without requiring proportional increases in overall resource consumption.
2Reliability
If conventional speech recognition approaches are used, then speech recognition can be performed, but error rate is high
Solution Approach 1:
The system is segmented into distinct functional modules (phoneme recognition, grapheme conversion, language modeling) that can be independently trained and optimized. This modular approach manages complexity by allowing each component to be developed and refined separately while contributing to overall system accuracy.
Solution Approach 2:
The system performs preliminary conversion of phonemes to graphemes before final speech recognition. By pre-processing the phoneme sequences through trained conversion models and language models, the system prepares refined representations that reduce errors in the final recognition stage, thereby improving reliability without proportionally increasing operational complexity.
3Measurement precision
If grapheme-based corrections are applied, then speech recognition accuracy is improved, but processing complexity increases
Solution Approach 1:
Graphemes serve as an intermediary representation that adds precision to the recognition process. The conversion from phonemes to graphemes through trained models provides a more refined intermediate state that improves measurement precision (recognition accuracy) while the automated nature of the conversion keeps processing complexity manageable.
Solution Approach 2:
The system creates copies of phoneme sequences in the form of grapheme representations. These grapheme copies are then processed through language models to generate corrected recognition outputs. This copying approach improves accuracy by allowing parallel processing and verification without requiring fundamentally more complex processing architecture.
Data Source
AI summary
Methods, systems, and computer program products for domain adaptive speech recognition using artificial intelligence are provided herein. A computer-implemented method includes generating a set of language data candidates, each language data candidate comprising one or more graphemes, by processing a sequence of phonemes related to input speech data using an artificial intelligence-based data conversion model; determining, for a target pair of phonemes and graphemes, a subset of graphemes from the set of language data candidates; generating a first speech recognition output by processing the subset of graphemes using at least one biasing language model and an artificial intelligence-based speech recognition model; generating a second speech recognition output by replacing at least a portion of the subset of graphemes in the first speech recognition output with at least one of the graphemes from the target pair; and performing automated actions based on the second speech recognition output.