Alphanumeric Speech Grammar Generation via Regular Expression
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Solution Overview
Problem
Current speech recognition systems face challenges in accurately recognizing alphanumeric concepts due to high perplexity and the inability to model diverse linguistic expressions, leading to recognition errors and the need for tedious and error-prone grammar development for specific alphanumeric concepts.
Innovation Solution
A method and system that generates a grammar for speech recognition by receiving a representation of an alphanumeric expression, such as a regular expression or mask, to automatically construct grammars that adapt to alternative spoken expressions and provide normalization, relieving developers from the complexity of grammar development.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a simple grammar based on a single state finite state model is used, then the grammar structure is simple, but the recognition accuracy deteriorates due to high perplexity and inability to capture domain-specific constraints
Solution Approach 1:
The patent segments the grammar into multiple states representing different phonetic patterns and linguistic expressions for alphanumeric concepts. Each state corresponds to a specific pattern (e.g., letter names, digit names, combinations), allowing the system to capture domain-specific constraints while maintaining manageable complexity through modular state design.
Solution Approach 2:
The patent introduces dynamic transitions between states based on phonetic patterns and linguistic context. The grammar adapts its structure dynamically by selecting appropriate states and transitions based on the input speech pattern, enabling accurate recognition of diverse alphanumeric expressions without requiring a completely complex static grammar.
2Ease of manufacture
If a simple finite state model is used, then the grammar is easy to implement, but it cannot model diverse linguistic expressions for alphanumeric strings
Solution Approach 1:
The patent creates a universal grammar framework that can handle multiple types of alphanumeric expressions (letter names, digit names, combinations, special character representations) within a single integrated state machine. The same grammatical structure accommodates diverse linguistic patterns by routing through different states, providing both ease of implementation and broad adaptability.
Solution Approach 2:
The patent embeds multiple levels of linguistic patterns within the grammar structure. Inner states handle specific phonetic patterns (e.g., individual letter or digit names), while outer states manage combinations and sequences. This nested arrangement allows the grammar to model diverse linguistic expressions by composing simpler pattern recognizers into more complex ones.
3Reliability
If developers manually author grammars for specific alphanumeric concepts, then the grammar can be optimized for the specific domain, but the development process becomes tedious and error-prone
Solution Approach 1:
The patent implements self-service through automated grammar generation from domain-specific templates and patterns. The system automatically creates the appropriate states and transitions based on input parameters (such as alphabet, digit sets, and linguistic patterns), eliminating the need for manual grammar authoring while maintaining domain optimization. This automation reduces development time and errors without sacrificing grammatical accuracy.
4Productivity
If grammar libraries and database entries are used, then the solution is readily available, but the orthographic form is not sufficient to serve as speech recognition grammar
Solution Approach 1:
The patent introduces an intermediary transformation layer that converts orthographic representations (from databases) into phonetic state machine grammars. This intermediary process translates written forms into appropriate speech recognition patterns, capturing the phonetic and linguistic characteristics needed for accurate speech recognition while leveraging the availability of orthographic data from existing databases.
Data Source
AI summary
A method and system to generate a grammar adapted for use by a speech recognizer includes receiving a representation of an alphanumeric expression. For instance, the representation can take the form of a regular expression or a mask. The grammar is generated based on the representation.