CODEBOOK.md

CODEBOOK - TonalityPrint Voice Dataset v1.0

Overview

This codebook provides definitions for variables, file naming conventions, and data structures in the TonalityPrint Voice Dataset v1.0.

Dataset Information:

• Total Files: 144 audio files + 144 JSON + 144 CSV + 1 combined CSV
• DOI: https://doi.org/10.5281/zenodo.17913895
• License: CC BY-NC 4.0
• Contact: ronda@TonalityPrint.com

Quick Navigation:

• File Naming Convention
• CSV Variables
• Tonality Indices
• Intention Categories
• Modifier Codes
• Segment-Level Data

---

File Naming Convention

Audio Files (.wav)

Structure:

[Version]_[Batch]_[Utterance]_[Type]_[Intention]_[Modifier]_[Ambivalence]_[Speaker].wav

Examples:

1. Single (Primary Intent only):
   TPV1_B1_UTT1_S_Att_SP-Ronda.wav

2. Compound (Primary Intent + Sub-modifier):
   TPV1_B1_UTT1_S_Reci_affi_SP-Ronda.wav

3. Complex (Primary Intent + Sub-modifier + Ambivalence):
   TPV1_B1_UTT1_S_Reci_affi_ambivalex_SP-Ronda.wav

Component Definitions

Component	Description	Valid Values	Example
Version	Dataset version	TPV1	TPV1
Batch	Batch number (1-6)	B1, B2, B3, B4, B5, B6	B1
Utterance	Utterance ID (1-18)	UTT1 through UTT18	UTT1
Type	Statement/Question	S (Statement), Q (Question)	S
Intention	Primary tonal intent	Att, Trus, Reci, Emre, Cogen, Baseneutral	Att
Modifier	Optional sub-modifier	See Modifier Codes	affi, calm
Ambivalence	Ambivalence marker	ambivalex (or omitted)	ambivalex
Speaker	Speaker identifier	SP-Ronda	SP-Ronda

---

CSV Variables (23 Columns)

Complete Variable List

The combined CSV file (ALL_TONALITY_DATA_COMBINED.csv) and individual CSV files contain these 23 variables:

#	Variable Name	Type	Description
1	Version	String	Dataset version identifier
2	Batch_Number	String	Batch identifier (B1-B6)
3	Utterance_Number	String	Utterance identifier (UTT1-UTT18)
4	Utterance_Type	String	S (Statement) or Q (Question)
5	File_Name	String	Complete audio filename
6	Primary_Intention	String	Primary tonal intent category
7	Sub_Modifier	String	Optional sub-modifier (or empty)
8	Ambivalex	String	Ambivalence marker (or empty)
9	Speaker	String	Speaker name
10	Utterance_Text	String	Transcribed utterance text
11	Trust_Index	Integer	Trust tonality score (0-100)
12	Reciprocity_Index	Integer	Reciprocity score (0-100)
13	Empathy_Resonance_Index	Integer	Empathy resonance score (0-100)
14	Cognitive_Energy_Index	Integer	Cognitive energy score (0-100)
15	Attention_Index	Integer	Attention score (0-100)
16	Notes	String	Annotation notes and observations
17	Duration	Time	Utterance duration (MM:SS format)
18	Date_Recorded	Date	Recording date (YYYY-MM-DD)
19	Source	String	Data source description
20	Segments	JSON String	Time-aligned segment data
21	Start_Time	Time	Utterance start time (MM:SS)
22	End_Time	Time	Utterance end time (MM:SS)
23	Timestamp	DateTime	ISO 8601 timestamp

---

Variable Definitions (Detailed)

Metadata Variables

1. Version

• Type: String
• Description: Dataset version identifier
• Values: "TPV1" (TonalityPrint Version 1)
• Example: TPV1

2. Batch_Number

• Type: String
• Description: Recording batch identifier
• Values: B1, B2, B3, B4, B5, B6
• Total Batches: 6
• Utterances per Batch: 18
• Example: B1

3. Utterance_Number

• Type: String
• Description: Unique utterance identifier within each batch
• Values: UTT1, UTT2, ..., UTT18
• Example: UTT1

4. Utterance_Type

• Type: String (Categorical)
• Description: Syntactic type of the utterance
• Values:
  • S = Statement (declarative sentence)
  • Q = Question (interrogative sentence)
• Distribution: ~83% Statements, ~17% Questions
• Example: S

5. File_Name

• Type: String
• Description: Complete audio filename with extension
• Format: TPV1_[Batch]_[Utterance]_[Type]_[Intention]_[Modifier]_[Ambivalence]_SP-Ronda.wav
• Example: TPV1_B1_UTT1_S_Att_SP-Ronda.wav

6. Primary_Intention

• Type: String (Categorical)
• Description: Primary functional tonal intent category
• Values:
  • Attention (directing focus and engagement)
  • Trust (conveying reliability and credibility)
  • Reciprocity (expressing mutual exchange)
  • Empathy Resonance (demonstrating empathetic connection)
  • Cognitive Energy (showing mental engagement)
  • Baseline Neutral (neutral control sample)
• Note: Full word used in CSV (e.g., "Attention"), abbreviated in filename (e.g., "Att")
• Example: Attention

7. Sub_Modifier

• Type: String (Optional)
• Description: Optional sub-modifier providing nuanced tonality descriptor
• Values: See Modifier Codes table
• Missing Data: Empty string if not applicable
• Example: affi (Affirming), empty string ""

8. Ambivalex

• Type: String (Optional)
• Description: Cross-modifier Ambivalence marker indicating mixed or transitional tonality
• Values:
  • ambivalex = Ambivalence present
  • Empty string = No ambivalence
• Definition: Two or more contradictory/competing sub-modifier layers present simultaneously
• Example: ambivalex, empty string ""

9. Speaker

• Type: String
• Description: Speaker identifier
• Values: Ronda
• Note: Single-speaker dataset (all 144 files same speaker)
• Example: Ronda

10. Utterance_Text

• Type: String
• Description: Verbatim transcription of spoken utterance
• Encoding: UTF-8
• Max Length: ~200 characters
• Example: "I want to make sure I understand what you need"

---

Tonality Indices (0-100 Scale)

All five tonality indices are measured on a continuous 0-100 scale where higher values indicate stronger presence of the measured tonal quality.

11. Trust_Index

• Type: Integer
• Range: 0-100
• Description: Quantified measure of trust tonality (perceived safety, authenticity, credibility)
• Interpretation:
  • Low (0-33): Uncertain, hesitant tonality
  • Moderate (34-66): Moderately reliable tonality
  • High (67-100): Highly trustworthy tonality
• Example: 75

12. Reciprocity_Index

• Type: Integer
• Range: 0-100
• Description: Quantified measure of reciprocal/collaborative tonality (inviting response, conversational balance)
• Interpretation:
  • Low (0-33): Unilateral communication
  • Moderate (34-66): Somewhat collaborative
  • High (67-100): Highly collaborative, balanced
• Example: 93

13. Empathy_Resonance_Index

• Type: Integer
• Range: 0-100
• Description: Quantified measure of empathetic tonality (emotional attunement, mirroring listener state)
• Interpretation:
  • Low (0-33): Detached, impersonal
  • Moderate (34-66): Moderately attuned
  • High (67-100): Highly empathetic, warm
• Example: 76

14. Cognitive_Energy_Index

• Type: Integer
• Range: 0-100
• Description: Quantified measure of cognitive engagement and mental energy (activation, momentum, pacing)
• Interpretation:
  • Low (0-33): Low engagement, slow pacing
  • Moderate (34-66): Moderate engagement
  • High (67-100): High mental energy, dynamic
• Known Issue: Shows systematic elevation across corpus (see Notes)
• Example: 96

15. Attention_Index

• Type: Integer
• Range: 0-100
• Description: Quantified measure of attentional focus (directing perceptual priority, maintaining engagement)
• Interpretation:
  • Low (0-33): Unfocused, diffuse attention
  • Moderate (34-66): Moderately engaged
  • High (67-100): Highly focused, commanding attention
• Example: 80

Scoring Methodology: All indices were scored by expert practitioner trained in "Tonality as Attention" framework based on perceptual assessment and acoustic analysis.

---

Additional Variables

16. Notes

• Type: String (Free text)
• Description: Annotation notes, quality observations, and systematic bias documentation
• Common Note: "Cognitive Energy (CE) seemingly exhibits systemic leaks/dominance, possibly due to speaker ecological style, lexical content and /or practitioner bias. Intentionally retained for transparency."
• Missing Data: Empty string if no notes
• Example: "Cognitive Energy (CE) seemingly exhibits systemic leaks/dominance..."

17. Duration

• Type: Time (MM:SS format)
• Description: Total duration of audio utterance
• Format: M:SS or MM:SS
• Range: ~3-6 seconds per utterance
• Total Duration: ~10 minutes (all 144 files)
• Example: 0:04 (4 seconds)

18. Date_Recorded

• Type: Date (YYYY-MM-DD)
• Description: Date the audio was recorded
• Date Range: December 19, 2025 - January 23, 2026
• Example: 2026-01-20

19. Source

• Type: String
• Description: Data source and annotation method
• Values: "Recording - Expert Practitioner Annotator"
• Note: All annotations performed by single expert practitioner
• Example: Recording - Expert Practitioner Annotator

20. Segments

• Type: JSON Array (stored as string in CSV)
• Description: Time-aligned segment-level tonality data with millisecond precision
• Structure: Array of objects with startTime, endTime, and five tonality indices
• See: Segment-Level Data Structure section
• Example: [{"startTime":0,"endTime":4284.083333333333,"trust":75,"reciprocity":93,"empathy":76,"cognitive":96,"attention":80}]

21. Start_Time

• Type: Time (MM:SS format)
• Description: Utterance start time (typically 0:00)
• Example: 0:00

22. End_Time

• Type: Time (MM:SS format)
• Description: Utterance end time (matches Duration)
• Example: 0:04

23. Timestamp

• Type: DateTime (ISO 8601 format)
• Description: Precise timestamp of annotation creation
• Format: YYYY-MM-DDTHH:MM:SS.sssZ
• Timezone: UTC (Z suffix)
• Example: 2026-01-20T16:45:24.342Z

---

Intention Categories

Primary Functional Tonal Intent States (6 Categories)

Category	Code (Filename)	Full Name (CSV)	Description
Attention	Att	Attention	Directing focus, capturing and maintaining listener engagement
Trust	Trus	Trust	Conveying trustworthiness, reliability, credibility, and authenticity
Reciprocity	Reci	Reciprocity	Expressing mutual exchange, collaborative communication, inviting response
Empathy Resonance	Emre	Empathy Resonance	Demonstrating empathetic connection, emotional attunement, warmth
Cognitive Energy	Cogen	Cognitive Energy	Showing mental engagement, cognitive processing, activation, momentum
Baseline Neutral	Baseneutral	Baseline Neutral	Neutral control sample, default prosody for comparative analysis

Capitalization Rules:

• First letter capitalized in filenames: Att, Cogen
• Full words in CSV: Attention, Cognitive Energy
• Baseline: Baseneutral (one word, capital B)

---

Modifier Codes (24 Optional Sub-Modifiers)

1. Trust Modifiers (5)

Code	Full Name	Description
auth	Authoritative	Commanding, expert tone
calm	Calm	Soothing, measured tone
conf	Confident	Self-assured, certain tone
rest	Formal/Respectful	Professional, courteous tone
reas	Reassuring	Comforting, supportive tone

2. Attention Modifiers (5)

Code	Full Name	Description
cert	Certainty	Confident, definite tone
clar	Clarity	Clear, precise communication
curi	Curious	Inquisitive, interested tone
focu	Focused	Concentrated, directed attention
urge	Urgent/Pressure	Time-sensitive, pressing tone

3. Reciprocity Modifiers (5)

Code	Full Name	Description
affi	Affirming	Validating, confirming tone
colla	Collaborative	Cooperative, team-oriented tone
enga	Engaged	Active, participatory tone
open	Open	Receptive, non-defensive tone
refl	Reflective	Thoughtful, contemplative tone

4. Empathy Resonance Modifiers (5)

Code	Full Name	Description
casu	Casual	Informal, relaxed tone
comp	Compassion	Kind, caring tone
corr	Corrective (softened)	Gentle correction or guidance
symp	Sympathetic	Understanding, supportive tone
warm	Warm	Friendly, approachable tone

5. Cognitive Energy Modifiers (4)

Code	Full Name	Description
ana	Analytical	Logical, reasoning-oriented tone
dyna	Dynamic	Energetic, active tone
enth	Enthusiastic	Excited, passionate tone
skep	Skeptical	Questioning, doubtful tone

Cross-Intent Modifier (1)

Code	Full Name	Description
ambivalex	Ambivalence	Mixed, transitional, or competing tonal cues present simultaneously

Capitalization Rule: All modifier codes are lowercase in filenames: affi, warm, ana, ambivalex

---

Segment-Level Data Structure

JSON Structure in "Segments" Field

Each utterance includes time-aligned segment-level tonality data stored as a JSON array string in the CSV.

Structure:

[
  {
    "startTime": <milliseconds>,
    "endTime": <milliseconds>,
    "trust": <0-100>,
    "reciprocity": <0-100>,
    "empathy": <0-100>,
    "cognitive": <0-100>,
    "attention": <0-100>
  }
]

Real Example:

[{
  "startTime": 0,
  "endTime": 4284.083333333333,
  "trust": 75,
  "reciprocity": 93,
  "empathy": 76,
  "cognitive": 96,
  "attention": 80
}]

Segment Field Definitions

Field	Type	Unit	Description
startTime	Float	Milliseconds	Segment start time from utterance beginning
endTime	Float	Milliseconds	Segment end time from utterance beginning
trust	Integer	0-100	Trust tonality score for this segment
reciprocity	Integer	0-100	Reciprocity score for this segment
empathy	Integer	0-100	Empathy resonance score for this segment
cognitive	Integer	0-100	Cognitive energy score for this segment
attention	Integer	0-100	Attention score for this segment

Notes:

• Most utterances contain a single segment (entire utterance)
• Times in milliseconds with decimal precision
• Segment scores may differ from utterance-level indices in multi-segment utterances
• To convert milliseconds to seconds: seconds = milliseconds / 1000

---

Missing Data Codes

How Missing Data is Represented

Field Type	Missing Data Representation
String fields (Sub_Modifier, Ambivalex, Notes)	Empty string ""
Numeric fields	No missing data (all utterances fully annotated)
Segments	No missing data (all utterances have segment data)

Important:

• There is NO use of -999, NULL, NA, or other special missing data codes
• Empty string "" indicates "not applicable" for optional fields
• All tonality indices are complete (no missing values)

---

Statistical Summary

Dataset Overview

Statistic	Value
Total Utterances	144
Total Batches	6
Utterances per Batch	18
Single Speaker	Yes (Ronda)
Language	English (American)
Recording Period	Dec 19, 2025 - Jan 23, 2026
Total Duration	~10 minutes

Audio Specifications

Specification	Value
Sample Rate	48,000 Hz
Bit Depth	16-bit
Channels	Mono (1)
Format	WAV (uncompressed PCM)
Duration Range	3-6 seconds per file

Index Distributions

Note: Actual statistical summaries (mean, SD, min, max) should be calculated from the complete dataset.

Expected Patterns:

• Cognitive_Energy_Index: Known systematic elevation (typically 90-100)
• Other indices: Expected to vary by Primary_Intention category
• See METHODOLOGY.md for quality control discussion

---

Known Issues & Limitations

Cognitive Energy Systematic Bias

Issue: Cognitive_Energy_Index shows systematic elevation across most utterances, regardless of Primary_Intention category.

Possible Causes (as noted in dataset documentation):

1. Speaker's ecological style (natural high-energy delivery)
2. Lexical content effects
3. Practitioner bias in scoring

Resolution: Intentionally retained for transparency and to reflect ecological reality of speech production. Researchers should account for this bias in analyses.

Impact:

• Trust and Empathy Resonance indices most affected
• Suggests need for speaker-specific normalization in some applications
• Does not invalidate other tonality measures

Single-Speaker Limitation

• All 144 files from same speaker (Ronda)
• Findings may not generalize to other speakers
• Multi-speaker extension needed for broader applicability

Controlled Environment

• Professional studio recordings
• May not reflect naturalistic speech conditions
• Scripted content (not spontaneous speech)

---

Usage Notes

Loading Data in Python

import pandas as pd
import json

# Load combined CSV
df = pd.read_csv('ALL_TONALITY_DATA_COMBINED.csv')

# Parse Segments JSON
df['Segments_Parsed'] = df['Segments'].apply(json.loads)

# Access first segment's trust score
first_segment_trust = df['Segments_Parsed'].iloc[0][0]['trust']

Loading Data in R

library(readr)
library(jsonlite)

# Load CSV
data <- read_csv('ALL_TONALITY_DATA_COMBINED.csv')

# Parse Segments JSON
data$Segments_Parsed <- lapply(data$Segments, fromJSON)

# Access segment data
first_segment <- data$Segments_Parsed[[1]][[1]]

Filtering by Intention

# Get all Attention utterances
attention_data = df[df['Primary_Intention'] == 'Attention']

# Get all utterances with ambivalence
ambivalent_data = df[df['Ambivalex'] == 'ambivalex']

# Get Trust utterances with calm modifier
trust_calm = df[
    (df['Primary_Intention'] == 'Trust') & 
    (df['Sub_Modifier'] == 'calm')
]

---

Citation

When using this dataset, please cite:

@dataset{polhill_2026_tonalityprint,
  author       = {Polhill, Ronda},
  title        = {TonalityPrint: A Contrast-Structured Voice Dataset for Exploring Functional Tonal Intent, Ambivalence, and Inference-Time Prosodic Alignment v1.0},
  year         = 2026,
  publisher    = {Zenodo},
  version      = {1.0.0},
  doi          = {10.5281/zenodo.17913895},
  url          = {https://doi.org/10.5281/zenodo.17913895}
}

---

Contact

Dataset Curator: Ronda Polhill
Email: ronda@TonalityPrint.com
DOI: https://doi.org/10.5281/zenodo.17913895

For questions about:

• Variable definitions → This codebook
• Annotation methodology → METHODOLOGY.md
• Dataset usage → DATACARD.md
• Technical issues → ronda@TonalityPrint.com

---

Version: 1.0.0
Last Updated: January 24, 2026
License: CC BY-NC 4.0