Published August 13, 2025 | Version v1

FAU speech-DPOAE data set for auditory attention

  • 1. ROR icon Friedrich-Alexander-Universität Erlangen-Nürnberg
  • 2. Friedrich-Alexander-University Erlangen-Nürnberg
  • 3. ROR icon Imperial College London

Description

Dataset Description: Speech-DPOAE Experiment

If you use this dataset, please cite our paper: doi: https://doi.org/10.1101/2025.08.15.670505

This dataset accompanies an experiment measuring distortion product otoacoustic emissions (DPOAEs) evoked by human running speech (speech-DPOAEs).  
Stimuli were designed to follow harmonic overtones n and m (n < m) of the speech signal’s fundamental frequency (f₀), with the expected lower-sideband DPOAE arising at harmonic 2n–m.

The experiment comprised:

  1. Single-speaker measurements – either male or female voice presented in isolation (two trials per participant).  
  2. Competing-speaker measurements – both voices presented simultaneously, with participants instructed to attend to one voice while ignoring the other (18 trials per participant).

 

Speech Signals

Two audiobooks were synthesized using a text-to-speech engine (ElevenLabs, U.S.A.) [1], with the voice “Matilda” for the female speaker and the voice “Brian” for the male speaker.

Source texts (in german language):  

  • Eine Frau erlebt die Polarnacht [4]   
  • Darum [2]  

Each text was synthesized with both voices, yielding:  `Darum_female`, `Darum_male`, `Polarnacht_female`, and `Polarnacht_male`.

Format and organization:   

  • Audiobooks are provided chapter-wise for single-speaker presentation. 
  • Mixed speech files for competing-speaker measurements are provided in `MixedBook_` folders.  
  • Each segment is ~2 minutes long.  
  • For single-speaker trials: 20 chapters per voice in total (2 chapters intended for single-speaker measurements, 18 chapters intended for subsequent mixing for competing-speaker measurements).
  • For competing-speaker trials: 18 chapters per audiobook presented simultaneously.

 

Stimuli for Speech-DPOAEs

Speech-DPOAE stimuli were computed using our recently developed method [5], pairing resolved resp. unresolved harmonic overtones n and m.  The lower-sideband cubic DPOAE arises at harmonic 2n–m.  

The table below lists n and m for the four stimulus types $F_{res}$ , $F_{unres}$ , $M_{res}$ , $M_{unres}$ :    

  n  m  2n-m 
$F_{res}$ 7 9 5
$F_{unres}$ 15 18 12
$M_{res}$ 6 8 4
$M_{unres}$ 15 18 12

 

Presentation protocol:

  • Single-speaker:  two stimulus pairs (resolved + unresolved, matching the presented voice).  
  • Competing-speaker:  four stimulus pairs (resolved + unresolved for both voices).

 

File organization:
Stimuli are stored in `Stimuli/`, sorted by book and voice.  

  • `SingleTwobandStimuli_xxx` – single-speaker two-band stimuli. 
  • `MixedTwobandStimuli_xxx` – competing-speaker two-band stimuli.
  • `Book_voice` - unmixed stimuli for chapters 1 to 18 of competing-speaker trials and DPOAE stimuli needed for evaluation.

 

Recordings

Stimuli were presented via an extended-bandwidth otoacoustic measurement system (ER10X, Etymotics, U.S.A.).  Audiobooks were delivered to the right ear, while stimulus presentation and DPOAE recordings were made in the left ear.  Recordings contain both the presented stimuli and the evoked DPOAEs.  

 
Data acquisition: automated via PsychoPy [3].
 
Folder structure:
  • `data_PXX/` – participant data.  
  • `PXX_timestamp_mic_stim_Attention_recorded/` – competing-speaker recordings (.wav).  
  • `PXX_timestamp_mic_stim_SingleSpeaker_recorded/` – single-speaker recordings (.wav).  
  • `PXX_timestamp.xlsx` – experiment metadata from PsychoPy.  
  • `PXX_targets_Attention.csv` – order of attentional targets.  
  • `PXX_targets_SingleSpeaker.csv` – single-speaker presentation order.  
 
In the `.csv` files, the stimulus column specifies the relative file path to the presented stimulus.
 
 

Code Availability

Code is available on GitHub:  
 
  • Stimulus creation  
  • Experiment evaluation  
  • DPOAE analysis
  • Statistical analysis of DPOAE morphology

 

References

[1] ElevenLabs, “ElevenLabs text-to-speech engine.” [Online]. Available: https://www.elevenlabs.io

[2] D. Glattauer, Darum. Paul Zsolnay Verlag, 2018.

[3] J. Peirce et al., “PsychoPy2: Experiments in behavior made easy,” Behav Res, vol. 51, no. 1, pp. 195–203, Feb. 2019, doi: 10.3758/s13428-018-01193-y.

[4] C. Ritter, Eine Frau erlebt die Polarnacht. Refinery, 2016.

[5] M. Saiz-Alía, P. Miller, and T. Reichenbach, “Otoacoustic Emissions Evoked by the Time-Varying Harmonic Structure of Speech,” eNeuro, vol. 8, no. 2, Mar. 2021, doi: 10.1523/ENEURO.0428-20.2021.

 

Notes

Please cite: Steinebach J, Reichenbach T. Attention to Speech Modulates Distortion Product Otoacoustic Emissions Evoked by Speech-derived Stimuli in Humans. submitted. bioRxiv 2025.08.15.670505; doi: https://doi.org/10.1101/2025.08.15.670505

Files

Audiobooks.zip

Files (9.3 GB)

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Additional details

Software

Repository URL
https://github.com/janna-stb/dpoae_attention_study.git
Programming language
Python
Development Status
Active