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Voice-to-Value Review System

AI system converting natural voice input to well-structured written reviews using LLMs with iterative refinement capabilities

Year:2024Type:publicationPublished in:ACM MUM 2024

Project Overview

Challenge

Creating quality online reviews required significant effort from users who had to carefully organize and articulate their thoughts, leading to low engagement with review platforms.

Solution

Built an innovative AI system that converts natural voice input into well-structured, coherent reviews using LLMs, making review creation effortless and natural for users.

Key Features

Voice input processing with speech-to-text conversion
AI-powered review enhancement using large language models
Longitudinal user study with 14 participants over one month
Iterative AI refinement system based on user feedback
Mobile application interface for on-the-go reviews
AI agent for review customization and rewriting
Integration with Google Reviews analysis research
Real-time voice-to-text processing

Results & Impact

Published research showing users prefer iterative AI refinement over one-shot improvements. Demonstrated through longitudinal study that voice-based review generation significantly improves user engagement and review quality.

14
Study Participants
1
Month Study Duration
ACM
MUM 2024 Published

Technology Stack

AI & ML

LLM APIs
Voice Recognition
Speech-to-Text APIs
Natural Language Processing
Mobile Development

Backend

Node.js

Frontend

React Native
JavaScript

System Architecture

Data Processing

Voice input processing and LLM enhancement

AI Engine

LLM-powered text enhancement and refinement

User Interface

Mobile application with voice input capabilities

Innovation

Novel approach to voice-based review generation with iterative AI refinement capabilities.

Performance

Validated through longitudinal study with 14 participants showing improved user satisfaction.

Impact & Results

Project Impact

Published research showing users prefer iterative AI refinement over one-shot improvements. Demonstrated through longitudinal study that voice-based review generation significantly improves user engagement and review quality.

Key Achievements:

Published in peer-reviewed ACM conference
Validated through longitudinal user study
Demonstrated improved user satisfaction
14
Study Participants
1 Month
Study Duration
ACM MUM
Publication Venue

Future Impact

Research findings will influence future voice interface design and AI-human interaction patterns in mobile applications.

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