Crowd-sourced Pronunciation Correction for Text-to-Speech Engines
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Solution Overview
Problem
Text-to-speech engines often produce errors in pronouncing words and phrases due to the complexity of phonetic rules across various cultural and linguistic sources, leading to mispronunciations that can impact user comprehension and trust in software applications.
Innovation Solution
A system that utilizes crowd-sourced pronunciation corrections aggregated through a central corpus, validated using game theory and data validation techniques, to provide validated pronunciation hints specific to locales and user classes, ensuring correct pronunciations that align with local norms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If phonetic rules are used to determine pronunciation of words or phrases, then TTS engines can handle words not in the dictionary, but pronunciation errors occur due to complexity of cultural and linguistic sources
Solution Approach 1:
The patent introduces an intermediary validation system that mediates between the phonetic rules and the final pronunciation output. This validation system checks and corrects pronunciations generated by phonetic rules, acting as a mediator to ensure accuracy while maintaining the ability to handle unknown words.
Solution Approach 2:
The patent implements a feedback mechanism where pronunciation corrections from users are collected, validated, and fed back into the system to improve future pronunciations. This feedback loop continuously improves pronunciation accuracy while maintaining versatility in handling new words.
2Reliability
If a dictionary of pronunciations is used for common words and phrases, then pronunciation accuracy improves for listed items, but the system cannot handle words or phrases not in the dictionary
Solution Approach 1:
The patent performs preliminary validation and correction of pronunciations before they are finalized and used. By validating pronunciations in advance through multiple checks and user feedback, the system ensures accuracy for both dictionary and non-dictionary words while preparing correction data for future use.
Solution Approach 2:
The system enables self-service through user feedback mechanisms where users can report pronunciation errors and suggest corrections. The system automatically processes these corrections through validation algorithms, allowing the system to self-improve its pronunciation capabilities without requiring manual reprogramming for each new word.
3Adaptability or versatility
If phonetic rules are written for non-indigenous languages or widely utilized dialects, then the rules can be applied broadly, but they cannot decode correct pronunciation of indigenous or local names
Solution Approach 1:
The patent applies local quality by implementing location-specific and culture-specific pronunciation rules and validation criteria. The system adjusts its validation standards based on the local context, allowing broadly applicable phonetic rules to be refined with local-specific corrections for indigenous names and local dialects.
Solution Approach 2:
The patent changes parameters such as validation thresholds, acceptable pronunciation variations, and correction criteria based on the specific cultural and linguistic context. This allows the system to maintain broad applicability while achieving high precision for local names by adjusting validation parameters according to the specific locale.
Data Source
AI summary
Technologies are described herein for providing validated text-to-speech correction hints from aggregated pronunciation corrections received from text-to-speech applications. A number of pronunciation corrections are received by a Web service. The pronunciation corrections may be provided by users of text-to-speech applications executing on a variety of user computer systems. Each of the plurality of pronunciation corrections includes a specification of a word or phrase and a suggested pronunciation provided by the user. The pronunciation corrections are analyzed to generate validated correction hints, and the validated correction hints are provided back to the text-to-speech applications to be used to correct pronunciation of words and phrases in the text-to-speech applications.