Predictive Speech Spelling with Dynamic Grammar
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Solution Overview
Problem
Existing user input methods for computing devices, such as cell phones and PDAs, are inadequate due to limited keyboard space and resource constraints, especially for speech recognition which is resource-intensive and requires significant training.
Innovation Solution
Predictive speech spelling allows users to spell out words letter-by-letter using dynamically updated speech recognition grammar and predictive language modeling, presenting probable symbols for user confirmation, conserving processing resources by limiting comparisons and interpolating with a uniform distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If speech recognition is used for text input on devices with limited keyboard space, then input efficiency is improved, but processing resources are excessively consumed
Solution Approach 1:
The patent segments the text input process into individual symbol recognition steps rather than processing complete words or phrases at once. The speech recognition system identifies one symbol at a time (letter, digit, or punctuation mark) and presents it to the user for confirmation, reducing the computational burden of processing entire speech sequences in one operation.
Solution Approach 2:
The system performs partial action by only processing and presenting the most probable symbol match to the user rather than generating and evaluating all possible speech recognition interpretations. This selective approach reduces processing resources while maintaining input accuracy through user confirmation of the top prediction.
2Adaptability or versatility
If word-based speech recognition is implemented, then text input capability is improved, but training requirements and complexity increase significantly
Solution Approach 1:
The patent simplifies the speech recognition task by segmenting it into symbol-level recognition rather than word-level recognition. This reduces the vocabulary size and complexity of the recognition model, thereby reducing training requirements while maintaining the ability to input any text through sequential symbol entry.
Solution Approach 2:
The system uses a pre-defined grammar structure that automatically organizes symbols into speakable sequences without requiring extensive user training. The predictive language model and grammar rules work together to guide the user through the input process, making the system self-adapting rather than user-adaptation-dependent.
3Measurement precision
If predictive language modeling with dynamic grammar updating is used, then prediction accuracy is improved, but processing overhead increases
Solution Approach 1:
The patent implements dynamic grammar updating where the speech recognition grammar is adjusted in real-time based on user confirmations and corrections. As users confirm or reject predicted symbols, the system learns and updates the grammar to better predict future inputs, improving accuracy while adapting to individual user patterns without requiring full reprocessing of the language model.
Solution Approach 2:
The system changes parameters of the prediction model dynamically by updating probability distributions and grammar weights based on observed user behavior. This allows the system to improve prediction accuracy by adapting to user-specific patterns while maintaining efficient processing through incremental parameter updates rather than complete model retraining.
Data Source
AI summary
The described implementations relate to speech spelling by a user. One method identifies one or more symbols that may match a user utterance and displays an individual symbol for confirmation by the user.