Domain-Specific Constrained Decoding for Speech Recognition Accuracy
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Solution Overview
Problem
Conventional speech recognition techniques often produce inaccurate and incomplete outputs when converting audio signals to text.
Innovation Solution
The method involves using an artificial intelligence-based automatic speech recognition model to process user-provided speech utterances and training a decoding engine constrained by a domain-specific model, enabling the generation of accurate domain-specific text and action outputs related to structured resources such as forms, by incorporating phonetic variations and domain information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional speech recognition techniques are used to convert audio signals to text, then the process is simple and fast, but the output accuracy and completeness deteriorate
Solution Approach 1:
The system segments the speech recognition process into multiple specialized components: an automatic speech recognition model for initial transcription, a domain-specific language model for contextual understanding, and a decoding engine for structured output generation. Each component handles a specific aspect of the recognition pipeline, improving overall accuracy while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The system merges multiple models and processing stages into a unified speech recognition system. The ASR model outputs are combined with domain-specific language model constraints, and the decoding engine integrates both to produce structured outputs. This merging of complementary components resolves the contradiction by achieving high accuracy through integration without creating unmanageable complexity.
2Reliability
If conventional speech recognition is used, then the system is easy to operate, but the output is inaccurate and incomplete
Solution Approach 1:
The decoding engine automatically adjusts its behavior based on domain-specific constraints and contextual information from the language model. The system self-regulates the decoding process by incorporating domain knowledge without requiring manual configuration or complex user intervention, thereby maintaining ease of operation while significantly improving output reliability.
Solution Approach 2:
The system incorporates feedback loops where the domain-specific language model evaluates ASR outputs and provides corrective guidance to the decoding engine. This feedback mechanism continuously refines the recognition accuracy by comparing against domain constraints and expected output formats, improving reliability while the automated feedback process maintains operational simplicity.
3Manufacturing precision
If domain-specific constraints and models are incorporated into the decoding engine, then text generation accuracy improves, but system complexity increases
Solution Approach 1:
The system applies domain-specific constraints and language models selectively to specific decoding scenarios and output types. Rather than uniformly applying complex constraints to all speech recognition tasks, the system tailors the level of constraint application to the specific domain and context, achieving high text generation precision for domain-specific tasks while avoiding unnecessary complexity for general purposes.
Solution Approach 2:
The domain-specific language model and constraints are pre-configured and trained before the actual speech recognition process. This preliminary preparation of domain knowledge and constraints allows the decoding engine to operate with high precision during inference without requiring complex real-time adjustments, thereby reducing operational complexity while maintaining text generation accuracy.
Data Source
AI summary
Methods, systems, and computer program products for automated domain-specific constrained decoding from speech inputs to structured resources are provided herein. A computer-implemented method includes converting at least a portion of at least one user-provided speech utterance into text by processing the at least one user-provided speech utterance using an artificial intelligence-based automatic speech recognition model; automatically training an artificial intelligence-based decoding engine, wherein automatically training the artificial intelligence-based decoding engine comprising constraining the artificial intelligence-based decoding engine based at least in part on a domain-specific model and the artificial intelligence-based automatic speech recognition model; and generating at least one of one or more domain-specific text outputs related to one or more structured resources associated with the domain and one or more domain-specific action outputs related to the one or more structured resources associated with the domain by processing at least a portion of the text using the artificial intelligence-based decoding engine.