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Project Requirements Document (PRD)

Project Name: SpoonerJS

Owner: Danny

Last Updated: (date)

1. Overview

1.1 Project Summary

SpoonerJS is a web-based application that generates spoonerisms by swapping phonemes rather than just letters. It will provide a visual, interactive experience where users can see and animate how words transform into their spoonerized counterparts.

1.2 Goals

  • Provide a fast, interactive web interface for generating spoonerisms.
  • Visualize the shift from words → phonemes → spoonerized phonemes → new words.
  • Improve usability by ranking results by word popularity.
  • Implement color-coded IPA symbols where colors make sense based on linguistic properties.
  • Ensure efficient client-side processing for a smooth user experience.
  • Create an API for programmatic access (optional).

2. Features

2.1 Core Features

Feature Description
Spoonerism Generator Accepts input text and returns valid spoonerisms based on phoneme swaps.
Phoneme Visualization Breaks words into phonemes, coloring them based on their linguistic category.
Animated Phoneme Swap Swaps phonemes in an animated transition.
Reverse Transformation Animates the shift back into words, making the change easy to follow.
Word Popularity Ranking Sorts spoonerisms by word frequency (using SUBTLEX, wordfreq, or a precomputed dataset).
Customizable Output Allows toggling between strict phoneme swaps vs. looser letter swaps.

2.2 Stretch Features (Nice-to-Have)

Feature Description
"Did You Mean?" Suggestions Auto-corrects typos and suggests spoonerisms for likely intended words.
Voice Input (Web Speech API) Users can speak phrases and generate spoonerisms via speech recognition.
Export Options Enables users to copy, download, or share results.
Mobile Optimization Ensures smooth performance on smartphones and tablets.

3. Technology Stack

Component Choice
Frontend Framework Vanilla JS (or React/Vue if needed)
Phoneme Processing CMU Pronouncing Dictionary (or equivalent in JS)
Word Frequency Ranking wordfreq.js (or a pre-built frequency dataset)
Visualization & Animation D3.js, GSAP (for smooth transitions)
Hosting Netlify / Vercel / GitHub Pages
Backend (if needed) Node.js + Express (optional for API)

4. UI/UX Requirements

UI Component Description
Text Input Box Users enter a phrase to generate spoonerisms.
Phoneme Breakdown Words transform into phonemes, each with a distinct color based on linguistic properties.
Phoneme Animation Phonemes swap positions with a smooth transition.
Reconstruction Animation The swapped phonemes morph back into new words.
Sorting & Filtering Users can sort by popularity or restrict results.
Copy/Share Button Allows users to easily copy or share results.

5. Phoneme Color-Coding System

Each IPA phoneme will be colored according to its linguistic category:

Phoneme Type Example Sounds Suggested Color
Vowels /a/, /e/, /i/, /o/, /u/ Blue (fluid, open sounds)
Nasal Consonants /m/, /n/, /ŋ/ Green (soft, flowing like air through the nose)
Stops (Plosives) /p/, /t/, /k/, /b/, /d/, /g/ Red (abrupt, explosive sounds)
Fricatives /f/, /v/, /s/, /z/, /ʃ/, /ʒ/, /θ/, /ð/ Yellow (continuous, airy sounds)
Affricates /tʃ/, /dʒ/ Orange (a mix of stop and fricative sounds)
Liquids /l/, /r/ Purple (smooth, flowing sounds)
Glides /w/, /j/ Cyan (semi-vowel, between vowel and consonant)

6. Animation Design

Animation Type Description
Phoneme Breakdown Words smoothly transition into individual phonemes.
Phoneme Swap Swapped phonemes move, fade, or morph in color to show the transformation.
Phoneme Reconstruction The new phoneme sequence forms the output words.

Animation Technologies

Approach Use Case
CSS Keyframe Animations Basic fading and transitions.
GSAP (GreenSock Animation Platform) Smooth, controlled animations for phoneme swaps.
D3.js (for phoneme trees) Interactive phoneme structure visualization.
Canvas/WebGL (Advanced) High-performance rendering for large datasets.

7. Performance & Scalability

Requirement Solution
Fast Execution Perform all spoonerism generation client-side using WebAssembly or optimized JS.
Efficient Lookup Store word frequency data in a compressed JSON file for quick lookups.
Low Latency Avoid unnecessary API calls; process everything in the browser.

8. Milestones & Timeline

Milestone Estimated Completion
Research NLP Libraries (Date)
Prototype Phoneme Swapper (Date)
Implement UI & Sorting (Date)
Deploy Beta Version (Date)
Launch Public Version (Date)

9. Open Questions

  • Which JavaScript NLP library is best for phoneme analysis? (e.g., CMUdict, natural)
  • Should we offer an offline mode with a cached dictionary?
  • How will we handle homophones and ambiguous cases?

10. Risks & Challenges

Risk Mitigation Strategy
Complex Phoneme Mapping Use an existing pronunciation dictionary to avoid re-inventing phoneme parsing.
Large Word Frequency Dataset Pre-load a compressed frequency list instead of calling APIs dynamically.
Cross-Browser Compatibility Test on Chrome, Firefox, Safari, and Edge.

11. Next Steps

  • Decide on the NLP phoneme library to use.
  • Build a basic phoneme swapper in JavaScript.
  • Create the UI prototype and test real-time results.