Hyperarticulated Vowel Mapping for Dyslexia Pronunciation Clarity
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Solution Overview
Problem
Languages with poor letter-to-sound mapping, such as English and French, pose challenges for learners and individuals with dyslexia in correctly pronouncing words due to complex rules and exceptions involving vowels, which existing text-to-speech systems fail to address effectively by providing unclear, non-hyperarticulated vowel sounds.
Innovation Solution
A system utilizing a natural language processing engine, syllabification engine, text-to-speech engine, and hyperarticulation rules engine maps hyperarticulated sounds to text units, displaying images corresponding to hyperarticulated vowels to aid learners in understanding correct pronunciations, with hyperarticulation rules engine applying specific rules to convert fluent vowel sounds into hyperarticulated forms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional text-to-speech systems are used, then fluency in speech is maintained, but vowel pronunciation clarity deteriorates for language learners
Solution Approach 1:
The patent segments the speech output by identifying and isolating vowel sounds from the continuous text-to-speech stream. The vowel segmentation module divides the fluent speech into discrete vowel units that can be individually processed and presented to learners, enabling precise pronunciation instruction without sacrificing the overall fluency of the original speech.
Solution Approach 2:
The patent applies different quality characteristics to different parts of the speech signal. Specifically, vowel sounds are extracted and presented with enhanced clarity and hyperarticulation, while consonants and other speech elements maintain their natural fluent characteristics. This allows learners to focus on vowel pronunciation without losing the natural flow of speech.
2Measurement precision
If complex pronunciation rules are provided to learners, then pronunciation accuracy may improve, but learning complexity and time requirement increase
Solution Approach 1:
The patent replaces the mechanical approach of teaching complex pronunciation rules with an acoustic signal processing approach. Instead of requiring learners to memorize and apply linguistic rules, the system uses signal processing techniques to extract, isolate, and present vowel sounds with enhanced clarity, allowing learners to acquire pronunciation skills through direct exposure to hyperarticulated vowel samples.
Solution Approach 2:
The patent creates simplified copies of vowel sounds by extracting them from fluent speech and presenting them in isolation with enhanced clarity. These vowel copies allow learners to study and practice individual pronunciation elements without dealing with the complexity of complete words or sentences, reducing learning time while maintaining accuracy.
3Measurement precision
If vowel sounds are extracted and presented in isolation, then pronunciation clarity improves, but contextual understanding of speech may deteriorate
Solution Approach 1:
The patent implements a feedback mechanism where learners can interact with the extracted vowel sounds. The system allows users to select specific vowels for detailed examination, providing on-demand clarification of pronunciation elements within their original contextual framework. This feedback loop enables learners to explore vowel clarity without permanently losing the contextual information.
Solution Approach 2:
The patent adds a new dimension to speech presentation by creating a layered structure where the original fluent speech coexists with extracted vowel highlights. Learners can access the hyperarticulated vowel information as an additional layer of detail without losing the original contextual speech, effectively moving from a single-dimensional presentation to a multi-dimensional learning experience.
Data Source
AI summary
In non-limiting examples of the present disclosure, systems, methods and devices for mapping hyperarticulated sounds to text units are presented. A plurality of textual units may be received. The plurality of textual units may be processed with a natural language processing engine. A sentence structure for the plurality of textual units may be identified, wherein the sentence structure comprises a plurality of words. The plurality of words may be processed with a text-to-speech engine. A text-to-speech output comprising a plurality of pronunciations may be identified, wherein each of the plurality of pronunciations corresponds to a syllabic unit of one of the plurality of words. A hyperarticulated vowel sound may be mapped to each syllabic unit from the text-to-speech output. A pronunciation instruction corresponding to each hyperarticulated vowel sound may be caused to be surfaced.