Phrase Transformation Rules for Natural Text-to-Speech
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Solution Overview
Problem
Conventional text-to-speech conversion methods often produce unnatural-sounding audio phrases due to a word-for-word conversion approach, which can be improved by analyzing and transforming text phrases to better match natural speech patterns.
Innovation Solution
Generating phrase transformation rules by analyzing features of source and target phrases to determine a mapping between words, allowing for the transformation of text phrases into more natural-sounding audio phrases, such as modifying word forms or reducing phrase length while retaining essence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If word-for-word text-to-speech conversion is used, then the conversion process is simple and straightforward, but the generated audio phrases sound unnatural
Solution Approach 1:
The system performs preliminary text processing by generating phrase transformation rules that rewrite text phrases before conversion to speech. This preliminary action modifies the text structure to match natural speech patterns, ensuring the audio output sounds natural while maintaining the simplicity of the overall conversion process
Solution Approach 2:
The system introduces an intermediary text transformation layer between the input text and the speech synthesis. This intermediary component generates transformed text phrases that serve as a bridge, converting unnatural word-for-word text into natural-sounding speech without requiring complex direct text-to-audio transformation
2Measurement precision
If phrase transformation rules are generated by analyzing feature sets of source and target phrases, then the accuracy of phrase transformation is improved, but the processing complexity increases
Solution Approach 1:
The system segments the text processing task into distinct components: extracting features from source phrases, extracting features from target phrases, determining word mappings, and generating transformation rules. This segmentation allows accurate feature analysis while managing complexity through modular processing steps
Solution Approach 2:
The system changes parameters by transforming text phrases according to identified patterns and rules. By modifying textual parameters (word forms, phrase structure, punctuation) based on the analyzed feature sets, the system achieves accurate transformation without requiring overly complex processing mechanisms
3Reliability
If text phrases are transformed to match natural speech patterns, then the quality of text-to-speech conversion is improved, but the processing time increases
Solution Approach 1:
The system performs text transformation as a preliminary action before speech synthesis. By pre-processing and generating phrase transformation rules in advance, the actual speech conversion can proceed more quickly, reducing overall processing time while maintaining high audio quality
Solution Approach 2:
The system uses self-service by automatically generating and applying phrase transformation rules without requiring manual intervention. This automation allows rapid transformation of text phrases while maintaining quality, as the system learns and applies patterns efficiently on its own
Data Source
AI summary
Method for text processing executable at a computing device, comprising appreciating a source phrase comprised of source words; appreciating a target phrase comprised of target words; associating a respective source word feature set with each one of the source words; associating a respective target word feature set with each one of the target words; analyzing source word feature sets and target word feature sets; and based on the analysis, generating one or more phrase transformation rules for transforming the source phrase into the target phrase. Also a server and non-transitory computer-readable medium storing program instructions for carrying out the method.