Symbol Sequence Conversion Using Confidence-Based Alignment Inference
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Solution Overview
Problem
Conventional techniques for converting symbol sequences, such as alphabetical character strings, into phonetic transcriptions in specific languages face challenges in accurately inferring alignment information and often produce unnatural readings or pronunciations due to inconsistent conversion accuracy.
Innovation Solution
A symbol sequence converting apparatus that generates candidate output symbol sequences based on rule information and derives confidence levels using a learning model to identify the most accurate output sequence, avoiding the need for explicit alignment information and improving conversion accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional techniques use alignment information for symbol sequence conversion, then conversion process is structured, but automatic inference of alignment information is difficult and conversion accuracy is inconsistent
Solution Approach 1:
The system performs self-service by automatically inferring alignment information between input symbol sequences and phonetic transcriptions without requiring external alignment data. The learning model autonomously discovers the correspondence relationships during training, eliminating the need for manual alignment preparation while achieving consistent conversion accuracy.
Solution Approach 2:
The patent introduces an intermediary alignment inference mechanism that bridges the input symbol sequence and phonetic transcription. This intermediary component automatically determines the correspondence relationships, serving as a mediator that resolves the mismatch between input and output without requiring pre-prepared alignment information.
2Ease of operation
If Seq2Seq framework is used for direct conversion, then alignment information is not required, but output symbol sequence may represent unnatural reading or pronunciation
Solution Approach 1:
The system implements feedback by using the learned alignment information to guide and refine the Seq2Seq conversion process. The alignment inference model provides feedback signals that help the conversion model generate more natural phonetic transcriptions by understanding the structural correspondence between input symbols and phonetic units, thereby improving output naturalness while maintaining operational simplicity.
3Measurement precision
If alignment information is manually prepared, then conversion accuracy improves, but automation level decreases and time consumption increases
Solution Approach 1:
The system achieves self-service by automatically inferring alignment information through machine learning, eliminating the need for manual preparation. The learning model autonomously discovers correspondence patterns between input symbol sequences and phonetic transcriptions, maintaining high conversion accuracy while maximizing automation and reducing time consumption.
4Ease of manufacture
If conventional conversion methods are used, then processing is straightforward, but unnatural pronunciations are output due to inconsistent accuracy
Solution Approach 1:
The patent combines multiple components into a composite conversion system that integrates alignment inference, Seq2Seq conversion, and phonetic generation. This composite approach merges the simplicity of rule-based methods with the adaptability of machine learning, maintaining ease of implementation while significantly improving pronunciation naturalness and conversion reliability.
Data Source
AI summary
A symbol sequence converting apparatus according to an embodiment includes one or more hardware processors. The processors: generates a plurality of candidate output symbol sequences, based on rule information in which input symbols are each associated with one or more output symbols each obtained by converting the corresponding input symbol in accordance with a predetermined conversion condition, the plurality of candidate output symbol sequences each containing one or more of the output symbols and corresponding to an input symbol sequence containing one or more of the input symbols; derives respective confidence levels of the plurality of candidate output symbol sequences by using a learning model; and identifies, as an output symbol sequence corresponding to the input symbol sequence, the candidate output symbol sequence corresponding to a highest confidence level.