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dataset.md

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+# DreamVoiceDB: Voice Timbre Dataset
+
+The DreamVoiceDB dataset is an integral part of our research on voice timbre and its diverse characteristics. This page provides a detailed overview of the dataset creation process, the criteria for speaker selection, the methodology for keyword annotation, and our approach to enhancing the dataset's richness and authenticity.
+
+## Overview
+
+Voice timbre is shaped by a myriad of factors, including age, gender, physical properties of the vocal tract and vocal cords, and perceptual characteristics. To effectively encapsulate the multifaceted nature of voice timbre, we employed a comprehensive three-stage process.
+
+
+#### Figure: Survey Method
+<img src="./data/survey_method.png" alt="Schematic diagram of DreamVoiceDB survey method" width="600"/>
+
+*Figure 1: Schematic diagram of DreamVoiceDB survey method.*
+
+
+## Details
+
+- **Datasets Used**: LibriTTS-R and VCTK
+- **Number of Speakers**: 900 [100 from VCTK and 800 from LibriTTS-R, sampled randomly]
+- **Keyword Selection Process**:
+  - Guided by an expert voice actor
+  - Total of 10 keywords identified
+  - Keywords categorized based on relative objectivity and subjectivity
+- **Annotator Profile**:
+  - Total Annotators: 8 (4 females and 4 males)
+  - Expertise: Speech pathology, accent coaching, singing coaching, transcription, and translation
+
+You can find our sample survey page used for data collection here - [DreamVoiceDB Survey Link](https://sm7orzxyhu.cognition.run/)
+
+## Keywords and Descriptors
+
+The DreamVoiceDB dataset utilizes a variety of keywords and descriptors to annotate voice timbre. These are grouped into relatively objective and subjective categories, along with additional aspects related to suitability for various voice-related professions.
+
+### Relatively Objective Keywords [Multtple Options Single Choice]
+- **Age**: 'Teenager or Young', 'Adult or Neutral', and 'Senior or Old'.
+- **Gender**: 'Definitely Female', 'Somewhat Ambiguous / Neutral'.
+- **Brightness**: Ranging from 'Very Dark (Rich)' to 'Very Bright (Thin)'.
+- **Roughness**: From 'Rough (Raspy)' to 'Smooth (Mellow)'.
+
+Reference Audio can be found in the following powerpoint presentation: [DreamVoiceDB Reference Audio](./data/reference_audio.pptx)
+
+### Relatively Subjective Keywords [Multiple Options Multiple Choice]
+- **Nasal**: A noticeable resonance or vibration in the nasal passages.
+- **Breathy**: You can hear the flow of air as the person speaks.
+- **Weak**: Lack of energy and confidence.
+- **Strong**: Full, resonant, and confident.
+- **Cute**: Perceived as young and endearing.
+- **Attractive**: A voice that draws pleasure in listeners.
+- **Cool**: Laid-back, confident, and collected.
+- **Warm**: A voice that conveys kindness, sincerity, and comfort.
+- **Authoritative**: A voice that carries weight and command.
+- **Others**: Other characteristics but not related to the speaker's emotion.
+
+### Additional Aspects
+This includes evaluating the voice's fit for professions such as singing, acting, public speaking, and other vocations where vocal qualities are paramount. Specific examples include:
+- **Public Presentation**: Public Speaker, Motivational Speaker, Teacher, Tour Guide
+- **Storytelling**: Podcaster, Audiobook Narrator
+- **Client and Public Interaction**: Customer Service, Healthcare Communicator, Retail Associate, Receptionist
+- **Diplomacy and Judiciary**: Diplomat, Judge
+- None of the above
+
+Each keyword and descriptor plays a crucial role in accurately capturing and conveying the unique characteristics of each voice sample in the dataset.
+
+### Analysis and Enhancement
+
+- Agreement scores used to prioritize keywords
+- Rigorous reassessment for moderately agreed keywords
+- For each speaker we atleast have age and gender keywords
+- Other keywords are selected based on the Agreement scores
+- We do not use any fine grained annotations for darkness and roughness in the model training
+- Use of OpenAI's GPT4 for natural language descriptors
+- Generation of descriptors based on all keyword combinations
+
+
+Generated Descriptors Examples:
+
+| Speaker ID | WAV File        | Keywords              | Prompt                                              |
+|------------|-----------------|-----------------------|-----------------------------------------------------|
+| 2562       | [File 2562](#)  | Male, Adult, Dark     | An adult male voice, with a dark timbre.            |
+| 30         | [File 30](#)    | Female, Teenager, Bright | A teenage girl's voice, radiating brightness and energy. |
+| 2156       | [File 2156](#)  | Male, Senior, Strong  | A senior male voice, characterized by strength and power. |
+
+
+## Dataset Statistics
+
+![Distribution](./data/stats/combined_all_pie_charts_single_row.png)
+![HeatMaps](./data/stats/updated_combined_heatmaps.png)
+![Keyword Distribution](./data/stats/equal_sized_combined_keywords_bar_plot.png)
+