Automatic Speech Grammar Construction via N-gram Analysis
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Solution Overview
Problem
Developing efficient speech recognition systems is hindered by the difficulty in constructing context-free grammars that provide high recognition accuracy, as they require precise matching of user speech to anticipated word sequences, and unified grammars are complex for application developers to design due to the need for extensive knowledge in scripting and probability computation.
Innovation Solution
A method and apparatus for automatically forming grammars by receiving example text strings, forming n-grams, and generating rules based on them, allowing for the construction of speech grammars without requiring extensive knowledge from developers, using an API with methods like AddSynonym, AddSemanticVariable, ProcessSentence, and Output to create a unified grammar format.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If context-free grammar is used for speech recognition, then the grammar structure is straightforward and easy to construct, but the recognition accuracy is limited because users must speak exact word sequences anticipated by the rule author
Solution Approach 1:
The patent transforms the static context-free grammar into a dynamic unified grammar that can adapt to various speech patterns. The grammar rules are made flexible by incorporating N-gram language models that dynamically adjust probability weights based on observed speech sequences, allowing the system to handle deviations from expected word orders while maintaining a structured grammar foundation.
Solution Approach 2:
The patent changes the parameters of grammar matching by introducing probability weights and backoff mechanisms. Instead of binary match/no-match decisions in CFG, the unified grammar uses continuous probability values that can be adjusted based on N-gram statistics, enabling gradual adaptation to different speech patterns and improving recognition accuracy.
2Measurement precision
If unified grammar is used to improve recognition accuracy and handle varying speech patterns, then the system can match speech with different word orders and omissions, but the complexity of grammar construction increases significantly requiring extensive knowledge in scripting and probability computation
Solution Approach 1:
The patent implements self-service by enabling the unified grammar to automatically learn from observed speech data. The system uses N-gram language models that automatically compute probability distributions from training corpora, eliminating the need for manual probability calculations by developers. The grammar structure self-adjusts based on observed speech patterns, reducing the knowledge burden on application developers.
Solution Approach 2:
The patent introduces N-gram language models as an intermediary layer between the structured grammar rules and the speech recognition process. This intermediary automatically handles the complex probability computations and backoff weight calculations, shielding application developers from the mathematical complexity while still enabling flexible speech pattern matching.
3Reliability
If unified grammar with N-gram language model is constructed, then backoff probabilities can be determined for better matching, but extensive knowledge is required to compute backoff weights and understand grammar interactions with library grammars
Solution Approach 1:
The system automatically computes backoff weights using N-gram language model training on speech corpora. The backoff mechanism is implemented as an automatic fallback process where the system learns from data which probability estimates are most reliable, eliminating the need for developers to manually tune these parameters while maintaining reliable speech matching.
Data Source
AI summary
A method and apparatus are provided for automatically forming a grammar. Example text strings are received and N-grams are formed based on the text strings. A rule in the grammar is then generated automatically based in part on the n-grams.