Datasets:
Throat Microphone Dataset
🤗 Hugging Face: pauljunsukhan/throatmic_codered
📦 GitHub: pauljunsukhan/throatmicdata
🚀 Fine-tuned Model:
Hardware Setup
- Throat Microphone: CodeRed Assault MOD Tactical Throat Mic Headset
- Uses standard 3.5mm audio jack
- Direct connection to MacBook Air's 3.5mm port
- Note: Many USB-C to 3.5mm microphone adapters are not compatible with throat microphones
Dataset Summary
A high-quality dataset of throat microphone (laryngophone) recordings specifically designed for fine-tuning Whisper and other speech recognition models. The dataset consists of 724 carefully selected English sentences recorded using a throat microphone, which captures speech through vibrations in the throat rather than air-conducted sound.
This dataset is particularly valuable for:
- Training ASR models for noisy environments
- Adapting speech recognition to throat microphone input
- Developing robust voice activity detection
- Research in alternative speech input methods
Dataset Statistics
- Total Recordings: 724
- Total Duration: 120.1 minutes
- Average Duration: 9.9 seconds (median: 10.0s)
- Duration Range: 5.9s - 11.0s
- Standard Deviation: 0.3s
Duration Distribution:
- <6s: 0.1% (1 recording)
- 6-8s: 0.8% (6 recordings)
- 8-10s: 98.6% (714 recordings)
- 10-12s: 0.4% (3 recordings)
- 12s or greater: 0%
Linguistic Characteristics
Vocabulary Statistics:
- Total Words: 9,334
- Unique Words: 3,629
- Vocabulary Density: 0.39
- Average Sentence Length: 13.0 words (range: 9-18)
Part of Speech Distribution:
- Nouns: 22.4%
- Proper Nouns: 12.1%
- Prepositions: 11.4%
- Verbs: 11.0%
- Determiners: 9.8%
- Adjectives: 9.1%
- Auxiliaries: 6.4%
- Pronouns: 5.2%
- Coordinating Conjunctions: 4.5%
- Adverbs: 4.3%
- Other: 13.8%
Complexity Metrics:
- Average Complexity Score: 8.0 (on a scale of 0-13)
- Average Tree Depth: 5.5
- Subordinate Clauses: 9.7%
- Relative Clauses: 9.7%
- Adverbial Clauses: 14.5%
Complexity Distribution:
- Simple: 0 sentences
- Moderate: 47 sentences (6.5%)
- Complex: 432 sentences (59.7%)
- Very Complex: 245 sentences (33.8%)
Supported Tasks
This dataset is suitable for:
- Automatic Speech Recognition (ASR): Training models to transcribe throat microphone audio
- Speech-to-Text: Converting throat microphone recordings to text
- Voice Activity Detection: Detecting speech in throat microphone signals
- Domain Adaptation: Adapting existing ASR models to throat microphone input
Languages
The dataset contains English-language recordings only, with:
- Standard American English pronunciation
- Academic and technical vocabulary
- Complex sentence structures
- High-quality transcriptions
Dataset Structure
Data Instances
Each instance in the dataset contains:
{
'audio': {
'path': str, # Path to the audio file
'array': np.array, # The audio signal array
'sampling_rate': int # 16000 (16kHz)
},
'text': str, # The transcription
'duration': float # Length in seconds
}
Audio Quality
All recordings meet strict quality requirements:
- Sample Rate: 16kHz mono
- Duration: Typically 8-10 seconds (98.6% of recordings)
- Audio Levels: -50dB to 0dB
- Signal-to-Noise Ratio: >10dB
- Silence Ratio: <30%
- Clean, professional recording environment
Data Fields
audio
: Audio file in WAV format (16kHz mono)text
: String containing the transcriptionduration
: Float value representing duration in seconds
Data Splits
The dataset is provided as a single training split.
Dataset Creation
Curation Rationale
This dataset was created to address the lack of high-quality throat microphone data for training speech recognition models. Throat microphones are particularly useful in noisy environments as they capture speech directly through throat vibrations.
Source Data
Initial Data Collection and Normalization
Sentences were carefully selected to ensure:
- Complexity suitable for model training (9-18 words)
- Proper grammar and punctuation
- Mix of statement types
- Natural language patterns
- Varied vocabulary
- Balanced phonetic content
Annotations
The annotations (transcriptions) are the original sentences used for recording, ensuring 100% accuracy.
Considerations for Using the Data
Social Impact of Dataset
This dataset can help improve speech recognition in:
- High-noise environments
- Military and emergency services communications
- Industrial settings
- Assistive technology for voice disorders
Discussion of Biases
The dataset:
- Contains only English language
- Uses standard English pronunciation
- May not represent all accents or dialects
- Recorded by a limited number of speakers
Other Known Limitations
- Limited to throat microphone recordings
- May not generalize well to regular microphone input
- Optimized for 8-10 second utterances
Additional Information
Dataset Curators
This dataset was curated by Paul Han
Licensing Information
This dataset is released under the MIT License.
Citation Information
If you use this dataset, please cite:
@misc{throatmic_dataset,
title={Throat Microphone Dataset for Speech Recognition},
author={Han, Paul},
year={2024},
publisher={Hugging Face},
howpublished={\url{https://huggingface.co/datasets/pauljunsukhan/throatmic_codered}}
}
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