candidate-parameters.md

Candidate Parameters for Future Implementation

This document outlines additional parameters that could enhance the dataset's research value for STT evaluation and fine-tuning. These parameters are candidates for future annotation phases.

Audio Signal Quality

Signal-to-Noise Ratio (SNR)

• Description: Computed dB ratio between speech signal and background noise
• Measurement: Automatic calculation from audio analysis
• Research Value: Critical for understanding STT performance degradation

Audio Clipping Detection

• Description: Whether audio peaks are clipped or distorted
• Options: [None, Minimal (<1% samples), Moderate (1-5%), Severe (>5%)]
• Research Value: Identifies recordings with digital distortion artifacts

Dynamic Range

• Description: Difference between loudest and quietest audio segments
• Measurement: dB difference between peak and RMS levels
• Research Value: Indicates recording quality and compression effects

Frequency Response Issues

• Description: Low-pass filtering effects from phone microphones
• Options: [Full Range, Slight Roll-off, Moderate Filtering, Heavy Filtering]
• Research Value: Understanding microphone limitations on STT accuracy

Speech Characteristics

Accent/Dialect Strength

• Description: How pronounced regional speech patterns are
• Options: [None/Standard, Slight, Moderate, Strong, Very Strong]
• Research Value: Evaluating STT robustness across dialects

Emotional State

• Description: Speaker's emotional state affecting speech patterns
• Options: [Calm, Excited, Frustrated, Tired, Stressed, Other]
• Research Value: Understanding how emotion affects STT accuracy

Speech Hesitations

• Description: Frequency of disfluencies and self-corrections
• Options: [None, Rare (<5%), Occasional (5-15%), Frequent (15-30%), Very Frequent (>30%)]
• Research Value: Testing STT handling of natural speech patterns

Articulation Quality

• Description: Clarity of speech production
• Options: [Very Clear, Clear, Slightly Unclear, Unclear, Mumbled]
• Research Value: Correlating articulation with transcription accuracy

Linguistic Complexity

Proper Noun Density

• Description: Percentage of words that are names, places, or brands
• Calculation: (Proper nouns / Total words) × 100
• Research Value: Evaluating STT performance on out-of-vocabulary terms

Domain-Specific Vocabulary

• Description: Presence of technical, specialized, or foreign terminology
• Options: [None, Low (<5%), Moderate (5-15%), High (15-30%), Very High (>30%)]
• Research Value: Testing STT adaptation to specialized domains

Sentence Structure Complexity

• Description: Grammatical complexity of spoken sentences
• Options: [Simple, Compound, Complex, Very Complex, Fragmented]
• Research Value: Understanding parsing challenges for STT systems

Out-of-Vocabulary (OOV) Rate

• Description: Estimated percentage of words not in common STT vocabularies
• Calculation: Based on comparison with standard word lists
• Research Value: Predicting STT performance on novel content

Recording Context

Device Movement

• Description: Movement pattern during recording
• Options: [Stationary, Slight Movement, Walking, In Vehicle, Other Motion]
• Research Value: Understanding motion effects on audio quality

Distance from Microphone

• Description: Estimated speaker distance from recording device
• Options: [Close (<6"), Normal (6-18"), Far (18-36"), Very Far (>36")]
• Research Value: Evaluating near-field vs. far-field performance

Recording App/Service

• Description: Application used for recording (affects preprocessing)
• Options: [Voicenotes.com, Native Voice Memo, WhatsApp, Zoom, Other]
• Research Value: Understanding preprocessing effects on STT

Time of Day

• Description: When the recording was made
• Options: [Early Morning, Morning, Afternoon, Evening, Late Night]
• Research Value: Correlating with voice fatigue and background patterns

STT Challenge Categories

Homophones Present

• Description: Words that sound alike but have different meanings
• Detection: Manual annotation or automated detection
• Research Value: Testing semantic disambiguation in STT

Code-Switching

• Description: Mixing languages within the same utterance
• Options: [None, Occasional Words, Phrases, Frequent Switching]
• Research Value: Multilingual STT robustness evaluation

Incomplete Sentences

• Description: Frequency of trailing off or interrupted thoughts
• Options: [None, Rare, Occasional, Frequent, Mostly Incomplete]
• Research Value: Natural speech pattern handling

Number/Date Format Complexity

• Description: Complexity of numeric and temporal expressions
• Examples: "March 3rd" vs "3/3/24", "twenty-five" vs "25"
• Options: [Simple, Mixed Formats, Complex, Ambiguous]
• Research Value: Numeric transcription accuracy evaluation

Implementation Priority

High Priority

• Signal-to-Noise Ratio (auto-computed)
• Emotional State (manual annotation)
• Proper Noun Density (semi-automated)

Medium Priority

• Device Movement
• Speech Hesitations
• Domain-Specific Vocabulary

Low Priority (Research Interest)

• Frequency Response Issues
• Code-Switching
• Sentence Structure Complexity

Notes

• Parameters marked as "auto-computed" can be calculated programmatically
• Manual annotation parameters should be added progressively
• Consider inter-annotator agreement studies for subjective parameters
• Some parameters may correlate strongly and could be combined or prioritized