Datasets:

litosway
/

MedicalLessons

segments listlengths 31 49
[ { "text": " Hey Everyone in this medical terminology lesson. I'm going to talk to you guys specifically about anatomical terms So this is actually a continuation of my medical terminology the basics lesson series So if you haven't watched that I would suggest you watch that first before you watch this one So in...
[ { "text": " Hey everyone, welcome to another medical terminology lesson. In this lesson we're talking about anatomical terms, and this is actually part two of the anatomical terms lesson series. So in this lesson we're going to talk about prefixes and suffixes that denote musculoskeletal anatomy and reproductiv...
[ { "text": " Hey everyone, JJ here. In this lesson, I'm going to be talking to you guys about medical terminology. Medical terminology is like learning a different language. It pretty much is like a different language. But there are many easy ways to break the word apart into different pieces to make it much eas...
[ { "text": " Hey everyone, this is medical terminology the basics lesson 3. So this is actually a continuation of lesson 1 and 2 and in this lesson we're going to talk more about some medical terminology with regards to medical conditions and medical processes and at the end of the lesson we're going to go throu...
[ { "text": " Hey everyone, welcome to yet another Medical Terminology The Basics lesson, and this is lesson four, so let's begin. So we're going to first look at more modifiers in this lesson. So first we're going to begin by looking at location modifiers. So the first one is axio, which means axial. You can thi...
[ { "text": " Hey everyone, here's another lesson in the Medical Terminology of the Basics series. This is Lesson 5. So in this lesson, we're going to talk about more basic but very important prefixes and suffixes that weren't covered in the previous lessons. So we're going to first discuss location and direction...

Dataset Card for Medical Lessons Speech Corpus

Dataset Description

Dataset Summary

The Medical Lessons Speech Corpus is a specialized audio dataset designed to benchmark Automatic Speech Recognition (ASR) systems on highly technical, domain-specific language. It contains a collection of medical lesson audio files accompanied by their corresponding transcriptions.

A defining characteristic of this dataset is its high density of complex medical terminology, making it an excellent stress test for evaluating the vocabulary coverage and phonetic accuracy of speech-to-text models. To ensure the highest standard of evaluation reliability, all transcripts have been strictly manually verified by the author, providing a highly accurate ground truth.

This dataset was originally created for the paper WhisperX with Context-Aware Transcription to evaluate state-of-the-art ASR models on challenging medical domain speech.

Supported Tasks

automatic-speech-recognition: The primary task is to accurately transcribe the spoken medical lessons into text. The dataset serves as a robust benchmark for evaluating domain-specific ASR performance.

Languages

The audio and transcriptions are in English (en).

Dataset Structure

Data Instances

A typical instance consists of an audio file and its corresponding ground truth transcript file in JSON format. The JSON transcript contains an array of speech segments with text and timing information.

Audio Format: Standard audio file (e.g., WAV or MP3). Transcript Format (.json):

[
  {
    "text": " Hey, everyone. Here's another lesson in the medical terminology the basic series...",
    "start": 0.031,
    "end": 21.04,
  },
  {
    "text": " The next one is acro, this means extremity...",
    "start": 22.137,
    "end": 51.753,
  }
]

Data Fields

text: The manually verified text transcript for the speech segment.
start: The start time of the speech segment in seconds.
end: The end time of the speech segment in seconds.

Dataset Creation

Annotation process

The ground truth transcripts were initially generated by openai whisper-1 model and then manually verified and corrected by the author. This rigorous manual review process ensures that all complex medical terms, drug names, and anatomical references are spelled accurately, providing a gold-standard reference for benchmarking.

Who are the annotators?

The manual verification was performed by the author of the paper XYZ, ensuring domain expertise in reviewing the medical terminology.

Considerations for Using the Data

Social Impact of Dataset

By providing a high-quality benchmark for medical ASR, this dataset encourages the development of more accurate speech-to-text systems for healthcare. Improvements in this domain can lead to better clinical documentation tools, enhanced accessibility for medical education, and reduced administrative burden for healthcare professionals.

Additional Information

Citation Information

If you use this dataset in your research, please cite the following paper:

@article{Author202X,
  title={WhisperX with Context-Aware Transcription},
  author={Carlos Bain},
  year={2026}
}

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