Spoken-to-Written Language Translation System
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Solution Overview
Problem
The differences between spoken and written language pose challenges in communication, such as the need for significant editing of transcribed spoken material to make it suitable for publication or understanding in oral delivery, due to variations in syntax, precision, and stylistic elements.
Innovation Solution
A method for translating between spoken and written language, involving speech recognition, mapping spoken utterances to formal utterances, and then to stylistically formatted written utterances, using databases and machine learning to account for subtle linguistic differences and individual variability, with the aid of parallel corpora and statistical inference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If speech recognition is used to transcribe spoken language, then conversion to written form is achieved, but the resulting text requires considerable editing to make it suitable for formal publication
Solution Approach 1:
The patent introduces an intermediary processing layer between speech recognition and final written output. This layer includes modules for discourse structure analysis, pragmatic inference, and stylistic transformation that mediate between the raw transcription and the formal written language, automatically performing the editing function that would otherwise require manual intervention.
Solution Approach 2:
The system changes linguistic parameters by detecting and transforming spoken language characteristics (such as hesitations, repetitions, informal syntax) into written language parameters (formal syntax, structured discourse, precise vocabulary). This parameter transformation allows the text to maintain the original meaning while achieving the formal quality required for publication.
2Manufacturing precision
If formal written language is used for oral delivery, then precision and integration are improved, but the delivery becomes more difficult to understand and sounds stilted
Solution Approach 1:
The patent applies inversion by reversing the traditional transformation direction. Instead of converting written to spoken, it converts spoken to written and then selectively transforms specific elements back toward spoken characteristics for oral delivery. This allows the text to maintain precision while incorporating natural spoken elements like appropriate pauses, emphasis markers, and conversational syntax.
Solution Approach 2:
The system applies local quality by making different parts of the text have different stylistic properties. Formal precise language is used for core content and definitions, while conversational elements are retained or added for transitions and explanations. This localized stylistic variation allows the text to be both precise and natural when delivered orally.
3Loss of time
If spoken language is transcribed without processing, then speed and authenticity are maintained, but the text lacks the structured syntax and precision of written language
Solution Approach 1:
The patent performs preliminary action by automatically analyzing and restructuring the transcribed text before final output. Discourse structure analysis and pragmatic inference modules process the text in advance to identify and correct syntactic issues, organize information hierarchically, and apply appropriate written language conventions, thereby eliminating the need for subsequent manual editing.
Solution Approach 2:
The system applies self-service by enabling the transcription process to automatically improve its own output. Through integrated natural language processing modules that analyze discourse structure, infer pragmatic meaning, and apply stylistic transformations, the system performs self-editing and self-correction, transforming informal speech patterns into formal written structure without external intervention.
Data Source
AI summary
Techniques for converting spoken speech into written speech are provided. The techniques include transcribing input speech via speech recognition, mapping each spoken utterance from input speech into a corresponding formal utterance, and mapping each formal utterance into a stylistically formatted written utterance.