RAWDysPeech: A Preprocessed Raw Audio Dataset For Speech Dysarthria

RAWDysPeech: A Preprocessed Raw Audio Dataset For Speech Dysarthria is a Speech Dysarthria Dataset for the applicaton of Audio Classification, Speech Detection and similar avenues of research in ASR.
RAWDysPeech consists of raw audio files segregated into two classes: 1 and 0, where 1 is for speech involving Dysarthria and 0 is for normal speech. We combine and preprocess some of the most popular speech datasets available open sourced. TORGO, UASPEECH, Ultrax, EasyCall are a few to be named.
Here's a brief description of the steps taken to preprocess and combine and we also encourage you to cite the original authors if this dataset helps in your research.
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This dataset provides preprocessed speech recordings from the UASPEECH database, specifically enhanced for machine learning applications using advanced noise reduction and signal processing techniques.

Dataset Description
The dataset contains audio recordings that have been processed using:

I. FFT-based noise reduction:
Hanning window application for better frequency analysis
16-bit audio depth processing
44.1 kHz sampling rate[1]
Stereo channel support with dual MEMS microphone configuration[2]

II. Preprocessing Steps
Signal Processing

Background noise subtraction using ambient noise sampling
Frequency spectrum analysis with FFT
Amplitude scaling and normalization
Single-sided FFT amplitude doubling for accurate frequency representation[1]

III. Audio Parameters

Bit Depth: 16-bit (pyaudio.paInt16)
Sample Rate: 44.1 kHz
Buffer Size: 44100 frames
Channel Configuration: Supports both mono and stereo recording[2]

IV. File Format
Audio files are saved in .WAV format
Timestamps are included in filenames (YYYY_MM_DD_HH_MM_SS_pyaudio)
Data is organized in dedicated data folders with automated directory creation[1]

V. Applications
Speech Recognition Systems
Dysarthric Speech Analysis
Audio Classification Tasks
Speech Pattern Recognition
Acoustic Model Training
Technical Implementation
The preprocessing pipeline includes real-time audio capture, noise profiling, FFT analysis, and spectrogram generation, making it suitable for both research and practical applications
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Citations:
[1] Heejin Kim, Mark Hasegawa Johnson, Jonathan Gunderson, Adrienne Perlman, Thomas Huang, Kenneth Watkin, Simone Frame, Harsh Vardhan Sharma, Xi Zhou, March 17, 2023, "UASpeech", IEEE Dataport, doi: https://dx.doi.org/10.21227/f9tc-ab45.
[2] Rudzicz, F., Namasivayam, A.K., Wolff, T. (2012) The TORGO database of acoustic and articulatory speech from speakers with dysarthria. Language Resources and Evaluation, 46(4), pages 523--541.
[3] Shah, Arya; Qureshi, Aymen; Polprasert, Chantri (2024), “ADAPTIVE: A Novel Dataset For Acoustic DysArthria deTection through temPoral Inference and Voice Engineering”, Mendeley Data, V1, doi: 10.17632/j5bgddf6rp.1