Published July 7, 2026 | Version v1
Dataset Open

OpenAMD: A Multilingual Voicemail and Answering Machine Dataset

Authors/Creators

  • 1. University of Sarajevo
  • 2. Infobip

Contributors

Supervisor:

  • 1. Infobip

Description

OpenAMD: A Multilingual Voicemail and Answering Machine Dataset

Dataset accompanying the paper: [paper link to be added].

Overview

The OpenAMD dataset is a publicly available dataset for non-commercial use. It contains audio recordings of humans and voicemails in several languages and under various augmentations in telephony-like settings.
 
The dataset contains in total 4,264 recordings of which 533 are unique recordings: 233 humans and 300 machines.
 

Basic audio information

All audio is sampled at 8kHz and encoded to one of the variants of the standard telephony codec G.711 [1]; either PCMA or PCMU. Recordings are usually a few seconds long

Dataset structure

The dataset contains files grouped into several directories with the following hierarchical layout:

open_amd/
├── open_amd.csv/
├── original/
│   ├── human/                 # 233 files
│   ├── machine/               # 300 files
├── noise_0dB/
│   ├── human/                 # 233 files
│   ├── machine/               # 300 files
├── noise_5dB/
│   ├── human/                 # 233 files
│   ├── machine/               # 300 files
├── noise_15dB/
│   ├── human/                 # 233 files
│   ├── machine/               # 300 files
├── noise_20dB/
│   ├── human/                 # 233 files
│   ├── machine/               # 300 files
├── packet_loss_low/
│   ├── human/                 # 233 files
│   ├── machine/               # 300 files
├── packet_loss_high/
│   ├── human/                 # 233 files
│   ├── machine/               # 300 files
 
 
 
The metadata regarding each file is located inside open_amd.csv. It contains the following information:
 
Column Description Example
filename Relative path to file, incldues both the channel condition and label of the file. noise_0dB/human/00f39e8c-7756-475c-a166-be0164564402_noise_0dB.wav
folder(augmentation)

Files are grouped into folders, depending on the channel condition: whether packet loss is present or whether static noise at various dB levels is present.

Can take only one of the following values:

  • original
  • noise_0dB
  • noise_5dB
  • noise_10dB
  • noise_15dB
  • noise_20dB
  • packet_loss_low
  • packet_loss_high

Each of the folders contains 533 files.

Packet loss was simulated using the Gilbert-Elliot model [2].

Noise is static, additive white Gaussian noise.

 packet_loss_low
language

Language of the file. Can be one of:

  • English: en (128)
  • Bosnian/Croatian: bs-hr (112)
  • German: de (77)
  • Dutch: nl (56)
  • Italian: it (53)
  • Chinese (Mandarin): zh (43)
  • Arabic: ar (43)
  • Turkish: tr (20)
 bs-hr
voice

Unique name (string identifier) of the speaker in case of human recordings, or name of Azure TTS neural voice [3] in case of voicemails. There are 8 unique human speakers and 44 synthetic voices.

 

 zh-CN-YunjianNeural
gender Associated speaker gender. Either M or F. M
text Transcript/spoken text in the recording. Evet, Kerem. Kim arıyor?
augmentation

Contains information about any additional augmentation applied to the recording. This was mostly done to voicemails to prevent models from overfitting. The augmentations are pretty self-explanatory and simulate common behavior in telephone conversations. Possible values:

  • no-aug
  • click
  • beep
  • ring
  • pause+beep
  • pause
  • ring+beep
  • click+beep

click+beep
codec

All audio is encoded to the telephony standard G.711 [1], either PCMA or PCMU variants.

 PCMU
ground_truth

True label of the file. Either HUMAN and MACHINE

 MACHINE
 
Additionally, the csv file contains the following columns (one for each model analyzed in the paper):
  • RNN-YAMNet [4]
  • CNN6-LogReg
  • CNN6-GBM-Balanced
  • CNN6-GBM
  • wav2vec-finetune [5]
  • whisper-GPT-5.4

These columns represent the predictions of those models for each given file. Consult the paper for more information about the models.

 

License

This dataset is released under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)  
You are free to share and adapt the material for any non-commercial use, provided appropriate credit is given.

Commercial use: contact authors for a separate license.

 

Contact

Feel free to contact any of the authors via: [name.surname]@infobip.com

 

References

[1] International Telecommunication Union: Pulse code modulation (PCM) of voice frequencies. ITU-T Recommendation G.711, International Telecommunication Union, Geneva, Switzerland (Nov 1988), https://www.itu.int/rec/T-REC-G.711-198811-I/en
[2] Pieper, J.: Relationships between gilbert-elliot burst error model parameters and error statistics. Tech. rep., Institute for Telecommunication Sciences (2023)
[3] Microsoft: Azure Speech Studio. https://speech.microsoft.com/ (2026)
[4] Altwlkany, K., Delalić, S., Selmanović, E., Alihodžić, A., Lovrić, I.: A recurrent neural network approach to the answering machine detection problem. In: 2024 47th MIPRO ICT and Electronics Convention (MIPRO). pp. 85–90. IEEE (2024)
[5] Downie, J.: wav2vec-vm-finetune. https://huggingface.co/jakeBland/wav2vec-vm-finetune (2025)

Files

open_amd.csv

Files (317.8 MB)

Name Size
md5:c3d1803058e616e27ab4b4033eea6e8f
969.2 kB Preview Download
md5:5df391877c5f11cdc6c3cdc22fc827f2
316.9 MB Preview Download

Additional details

Dates

Available
2026-07-07