SautiDB: Nigerian Accent Dataset Collection

• Main Authors: Afonja, Tejumade, Mbataku, Clinton, Malomo, Ademola, Okubadejo, Olumide, Francis, Lawrence, Nwadike, Munachiso, Orife, Iroro
• Format: Other/Unknown Material
• Language: Old English
• Published: Zenodo 2021
• Subjects: accent; nigerian_accent; african_accent; audio; voice; Arctic
• Online Access: https://doi.org/10.5281/zenodo.4561842

The SautiDB dataset collection projectis an ongoing effort to collect datasets of various Nigerian accents. The dataset was collected in an uncontrolled manner, users who visit our webapp can recordtheir voice and contribute to the dataset. The webapp uses the audio webapi to collect voice samples. We hope this dataset will be useful to people interested in developing voice technology in Nigeria. We will continuously collect more datasets and publish updated versions as we have them. This work grew out of our project Improving Online Experience using Accent Transfer . The filename is of the form nativeLanguage_fluentLanguage_speakerID_gender_sentenceID.wav, where nativeLanguage: language spoken by the speaker's tribe. Native (mother) language of the speaker fluentLanguage: language that the speaker feels best describes their accents speakerID: ID, assigned to the speaker. It is possible for a speaker to have multiple IDs assigned since we are not authenticating users, we simply cached their browser sessions. gender: gender of the speaker. We did not explicitly collect this information from users, wehand-labeled it. sentenceID: the sentence ID for the sentences read. We used the CMU Arctic sentences . =========================== Before Postprocessing =========================== Number of Samples: 1615 Size Webm: 59MB Size Wav: 847MB Sampling Rate: 48000Hz Total Time: 2hrs 30min 21sec ============================ After Postprocessing ============================ Number of Samples: 919 Size Wav: 336MB Sampling Rate: 48000Hz Total Time: 0hrs 59min 08sec The associated Github repository used for post-processing can also be found linked . We are grateful for funding from AI4D-IndabaX with IDRC Grant Number: 109187-002. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License .