Speech Recognition Using Segmented Vocabulary Search
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Solution Overview
Problem
Existing speech recognition methods for embedded systems in vehicles, such as navigation devices, face challenges in accurately recognizing multi-part utterances like addresses due to limited computing and memory resources, often requiring segmentation and manual user selection, leading to less reliable and inconvenient user operations.
Innovation Solution
The method employs a first vocabulary that includes combinations of elements and filler words for forward and backward searches, allowing the entire utterance to be used in the first search step, eliminating the need for segmentation and enabling natural language input without manual selection, while reducing resource demands by using a second vocabulary for further recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the entire utterance is used in the first search step, then recognition accuracy is improved, but memory and computing power requirements increase
Solution Approach 1:
The vocabulary is segmented into two levels: a first vocabulary containing only search elements (e.g., city names) for the first search step, and a second vocabulary containing complete multi-part utterances (e.g., full addresses) for the second search step. This segmentation allows the system to perform initial matching with reduced memory requirements, then expand to complete vocabulary matching only for promising candidates, thereby resolving the contradiction between recognition accuracy and resource consumption.
2Quantity of substance
If utterances are segmented into parts for recognition, then resource requirements are reduced, but user convenience deteriorates due to manual selection
Solution Approach 1:
The system performs preliminary speech recognition on segmented parts of the utterance to generate a reduced set of candidate hypotheses. These preliminary results are then used to construct a context-specific second vocabulary for the final recognition step. This preliminary action eliminates the need for manual user selection while maintaining resource efficiency, as the system automatically processes only the most relevant vocabulary items in the second search step.
3Power
If a restricted search space is used, then computing power requirements are reduced, but speech recognition reliability decreases
Solution Approach 1:
The search space dynamically expands from a restricted first vocabulary to a comprehensive second vocabulary based on the results of the first search step. The system initially uses a small, context-specific vocabulary to reduce computing power requirements, then automatically expands to include complete multi-part utterances for final recognition. This dynamic adaptation ensures both computational efficiency and recognition reliability without requiring manual intervention.
Data Source
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AI summary
The method involves assigning a statement from a detection lexicon to a best-evaluated searching element, and assigning statement from another detection lexicon to another search element by using beam search algorithm. Hypotheses of vocabulary are formed with the searching elements and filler words. Best-evaluated hypothesis is determined from the hypotheses of vocabulary based on production probabilities for the entire statement. Residual cost evaluations are used for calculating the production probabilities for a part of the statement. Independent claims are also included for the following: (1) a device for speech recognition of statement (2) a computer program product for executing a method for speech recognition of statement.