Language Model Generation Using Transaction Data
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Solution Overview
Problem
Existing speech recognition systems lack the ability to adapt and improve accuracy based on user-specific interactions and transaction data, leading to suboptimal performance in recognizing user-specific terms and phrases.
Innovation Solution
The system generates and maintains language models based on transaction data, incorporating user-specific language elements and updating them dynamically to improve recognition accuracy by associating phonemes with letters, numbers, words, and phrases, and adjusting probabilities based on usage context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If language models are generated based on transaction data to improve speech recognition accuracy for user-specific terms, then recognition accuracy is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary actions by generating language models in advance based on transaction data before speech recognition is needed. The language model generation module creates models using phoneme associations, letter mappings, and word/phrase probabilities derived from historical transaction data, so that when speech recognition occurs, the model is already prepared and tuned to the user's specific terminology and context.
Solution Approach 2:
The patent introduces a language model as an intermediary between the raw speech input and the recognition system. This language model acts as a mediator that translates user-specific transactional language into a format the speech recognition system can process, containing phoneme-letter mappings, word-phrase associations, and contextual probabilities that bridge the gap between generic speech recognition and user-specific accuracy.
2Adaptability or versatility
If language models are dynamically updated based on user interactions, then adaptability is improved, but processing time increases
Solution Approach 1:
The system performs preliminary actions by continuously updating the language model in the background using transaction data as it becomes available. The model generation module proactively incorporates new phoneme associations, letter mappings, and word/phrase probabilities from user interactions before they are needed for recognition, so the model is always ahead of the curve without causing delays during actual speech recognition events.
Solution Approach 2:
The language model updating operates continuously rather than in discrete batches. As transaction data accumulates from user interactions, the model generation module continuously refines the phoneme associations, letter mappings, and contextual probabilities, ensuring the language model remains current and adaptive without interrupting the speech recognition workflow.
3Measurement precision
If comprehensive transaction data is used to generate language models, then recognition accuracy is improved, but data processing requirements increase
Solution Approach 1:
The system extracts only the essential and relevant features from comprehensive transaction data to build the language model. Rather than processing all raw transaction data, the model generation module extracts key elements such as phoneme associations, letter mappings, word/phrase probabilities, and contextual relationships, discarding redundant information while retaining the critical components needed for accurate user-specific speech recognition.
Solution Approach 2:
The language model applies local quality by creating specialized representations for specific user contexts and terminology. Instead of a generic model, the system generates localized phoneme associations, letter mappings, and word/phrase probabilities tailored to each user's transactional language patterns, ensuring high accuracy for user-specific terms while maintaining efficiency by not processing unnecessary global data.
Data Source
AI summary
Described herein are systems and methods for the generation and maintenance of language models. Language models are developed based at least in part on transaction data from one or more users. These transactions may include purchases and other interactions between one or more users and one or more online merchants. The language models may be associated with a particular user or group of users. Ongoing transactions may modify the language models. The language models may be used to recognize spoken input from the one or more users.