Adaptive Text-to-Speech With Feedback-Based Contextual Speech Patterns

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Solution Overview

Problem

Existing communication technologies struggle to adapt to diverse user needs and nuances, such as sarcasm, impacting user interaction and perception, particularly in customer service scenarios.

Innovation Solution

A computing system that utilizes adaptive, individualized, and contextualized text-to-speech processing, incorporating natural language processing and machine learning to categorize user inputs, generate responses, and adjust speech patterns based on user feedback for optimal outcomes.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If existing text-to-speech technologies are used, then basic speech conversion is achieved, but the system cannot adapt to diverse user needs and nuances such as sarcasm

Engineering Contradiction:

Improveadaptability to user needsVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system segments the text-to-speech process into multiple independent modules: audio data reception, natural language processing, context categorization, response generation, speech pattern selection, and user feedback measurement. Each module handles a specific aspect of the communication, allowing the system to adapt to diverse user needs while maintaining manageable complexity through modular architecture.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system dynamically adjusts speech patterns based on real-time user feedback and context analysis. The speech synthesizer modifies tone, tempo, and style according to the categorized context and measured user reactions, enabling adaptability without requiring complete system redesign for each user scenario.

Inventive Principle:

Principle #15Dynamics

2Measurement precision

If existing communication technologies are used, then basic interaction is possible, but nuanced meanings such as sarcasm cannot be detected

Engineering Contradiction:

Improvedetection accuracyVSAvoidprocessing complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system introduces natural language processing and context categorization as intermediary layers between audio input and speech output. These intermediaries analyze and interpret nuanced meanings including sarcasm before generating appropriate responses, improving detection accuracy while isolating the complexity within dedicated processing modules.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system replaces basic pattern-matching mechanisms with advanced natural language processing and machine learning models. This substitution enables precise detection of nuanced communication elements like sarcasm by utilizing computational linguistics and contextual analysis rather than simple keyword matching.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Adaptability or versatility

If standardized speech patterns are used, then consistent output is produced, but individualized and contextualized responses cannot be provided

Engineering Contradiction:

Improvecontextualization capabilityVSAvoidresponse generation speed

Core Design Contradiction:

Adaptability or versatilityVSProductivity

Solution Approach 1:

The system performs preliminary context categorization and speech pattern selection based on analyzed user input before generating the final response. By pre-processing and classifying the communication context, the system prepares appropriate speech patterns in advance, enabling contextualized responses without significantly delaying overall response generation.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system measures user reactions to speech patterns and uses this feedback to continuously refine and adjust future responses. This feedback loop enables the system to learn from interactions and improve contextualization over time while maintaining efficient response generation through optimized processing paths.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20250218422A1Adaptive, individualized, and contextualized text-to-speech systems and methods

Publication Date: 2025.07.03 TRUIST BANK

AI summary

Systems and methods receive, in real-time from a user via a user device, input audio data comprising communication element(s) and trained model(s) are applied thereto to categorize the communication element(s), the categorizing comprising assigning a contextual category to a communication element. Text is generated that includes a response to the communication element(s), the response including individualized and contextualized qualities predicted to provide an optimal outcome based on (i) the assigned contextual category and (ii) the user. Text-to-speech processing of the text is implemented to produce an audio output comprising (a) the response and (b) a speech pattern predicted to facilitate the optimal outcome. The audio output is provided to the user via the user device, and based thereon a user's reaction is measured according to a quantifiable quality score that is used to modify future iterations of text-to-speech processing to provide future audio output(s) including a revised speech pattern.