Speech Recognition for Alphanumeric Data Inputs
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Solution Overview
Problem
Conventional speech recognition technologies face challenges in accurately recognizing alphanumeric data inputs, such as IDs and dates, due to the combination of letters and numbers, which leads to inaccurate translations.
Innovation Solution
The method involves segmenting data inputs into n-gram chunks, generating and training a language model based on metadata characteristics, and creating variations to improve the accuracy of speech recognition for alphanumeric inputs, using techniques like regex algorithms and machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional speech recognition technology is used to recognize alphanumeric data inputs, then the system can process speech data, but the accuracy of translation is poor due to confusion between letters and numbers
Solution Approach 1:
The patent segments the speech recognition process into distinct components: acoustic model for sound-to-phone conversion, language model for contextual understanding, and post-processing module for alphanumeric disambiguation. This segmentation allows each component to specialize in specific aspects, improving overall accuracy for alphanumeric inputs
Solution Approach 2:
The patent introduces intermediate representations (phonetic transcriptions, n-gram sequences, and confidence scores) as mediators between the acoustic model and final text output. These intermediaries allow the system to process and disambiguate alphanumeric sequences through multiple processing stages rather than direct conversion
2Adaptability or versatility
If speech recognition system uses general language models, then it can handle various speech inputs, but it fails to accurately differentiate between letters and numbers in alphanumeric data
Solution Approach 1:
The patent applies local quality by using context-dependent processing: general language models handle conversational speech while specialized alphanumeric models handle data inputs. The system adapts its processing approach based on the local characteristics of the input, applying appropriate differentiation rules for letters and numbers in specific contexts
Solution Approach 2:
The system dynamically switches between different recognition modes and model configurations based on input characteristics. It adjusts language model parameters, n-gram orders, and disambiguation strategies in real-time according to the detected speech pattern, maintaining versatility while improving alphanumeric accuracy
3Measurement precision
If the system segments data into n-gram chunks and generates language model variations, then the contextual coverage improves, but the computational complexity and processing time increase
Solution Approach 1:
The patent uses partial action by applying n-gram modeling selectively rather than to all speech inputs. It generates language model variations of different orders (bigrams, trigrams, etc.) and applies them based on context requirements, avoiding the excessive computational burden of using the most complex models for every case
Solution Approach 2:
The system changes parameters dynamically by adjusting n-gram order, language model vocabulary size, and processing depth based on input characteristics. This allows the system to optimize the balance between contextual coverage and computational complexity for different speech recognition scenarios
Data Source
AI summary
A method, system, and computer program product for recognizing, and training to recognize, alphanumeric speech data inputs. The method may include segmenting a data input into a sequential n-gram chunks based on a predetermined rule, where the data input is received through speech recognition. The method may also include receiving metadata regarding characteristics of the data input. The method may also include generating a language model based on the metadata. The method may also include generating a first set of language model variations of the data input. The method may also include training the language model based on at least the first set of language model variations. The method may also include generating one or more alternatives for the data input using the trained language model. The method may also include transmitting an output including the one or more alternatives for the data input.