metadata

language:
  - en
license: mit
task_categories:
  - automatic-speech-recognition
task_ids:
  - audio-intent-classification
pretty_name: Throat Microphone Dataset
size_categories:
  - 1K<n<10K
tags:
  - audio
  - speech
  - throat-microphone
  - whisper
  - asr
dataset_info:
  features:
    - name: audio
      dtype:
        audio:
          sampling_rate: 16000
    - name: text
      dtype: string
    - name: duration
      dtype: float32
  splits:
    - name: train
      num_bytes: 336025
      num_examples: 1
  download_size: 338241
  dataset_size: 336025
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*

Throat Microphone Dataset

🤗 Hugging Face: pauljunsukhan/throatmic_codered
📦 GitHub: pauljunsukhan/throatmicdata

🚀 Fine-tuned Model:

Dataset Description

Homepage: GitHub Repository
Repository: Hugging Face Repository
Paper: N/A
Point of Contact: Paul Han

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 transcription
duration: 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}}
}