Common Voice Scripted Speech 26.0 - Ewondo

ewo — Ewondo (ewo)

This datasheet is for cv-corpus-26.0-2026-06-12 of the Mozilla Common Voice Scripted Speech dataset for Ewondo [ewo - ewo]. The dataset contains 7991 clips representing 14.36 hours of recorded speech (13.61 hours validated) from 31 speakers, recorded from a text corpus of 883 sentences.

Language

Ewondo is a Narrow Bantu language which is indigenous to a population mainly located in the Centre Region of Cameroon, with pockets of settlements in the South, and East Regions. Having been one among the earlier developped languages within the so-called Beti-Fang group - with comparable earlier development to be found in the Bulu language of the same Beti-Fang group, Ewondo is vehicular to populations in the South and East Regions of Cameroon, and has also developed into a creole known as Mongo Ewondo;

Variants

The contributors to this dataset have listed the following Ewondo varieties: Kolo, Ewondo (also known as Yewondo), and Bene.

Demographic information

The dataset includes the following self-declared age and gender distributions. A coverage summary is shown below each table.

Gender

Self-declared gender information. The table shows clip and speaker counts with percentages. Speakers who did not declare a gender are listed as Unspecified. A dash (-) indicates zero.

Gender declared: 73 of 7,991 clips (0.9%), 3 of 31 speakers (9.7%)

Age

Self-declared age information. The table shows clip and speaker counts with percentages. Speakers who did not declare an age are listed as Unspecified. A dash (-) indicates zero.

Age declared: 453 of 7,991 clips (5.7%), 5 of 31 speakers (16.1%)

Data splits for modelling

Clip buckets

Training splits

Training split coverage: 883 of 7,571 validated clips (11.7%)

The dataset contains 7571 validated, 318 invalidated, and 102 unresolved clips. The average clip duration is 6.472 seconds.

Text corpus

Validated sentences: 883

The corpus contains 883 sentences: 883 validated and 0 unvalidated (0 pending review, 0 rejected), with 0 reported for review.

Writing system

The writing system used for the collection of sentence prompts for read speech in this dataset is based on the General Alphabet of Cameroonian Languages, though it lacks tone marking.

Sample

There follows a randomly selected sample of five sentences from the corpus.

1. Yuum a Nkɔg andǝŋ a zaŋ yob ai si.

2. Awulu anə məki moyəm, otui mvog omini məvɔg.

3. A ngaa- sug Kulu nyaa a komoŋ nye bibag ai fa.

4. Fəg e dzeŋa Mlomodo, a ayem naa a abɔ ya.

5. Ndɔ bə ngatie ai dulu.

Sources

Fields

Clips

Each row of a tsv file represents a single audio clip, and contains the following information:

• client_id - hashed UUID of a given user

• path - relative path of the audio file

• sentence - the sentence to be read aloud

• sentence_id - unique identifier for the sentence

• sentence_domain - domain classification(s) of the sentence

• up_votes - number of people who said audio matches the text

• down_votes - number of people who said audio does not match text

• age - age of the speaker1

• gender - gender of the speaker1

• accents - accents of the speaker1

• variant - variant of the language1

• locale - locale code of the language

• segment - if sentence belongs to a custom dataset segment, it will be listed here

validated_sentences.tsv

The validated_sentences.tsv file contains one row per validated sentence in the text corpus:

• sentence_id - unique identifier for the sentence

• sentence - the sentence text

• variant - the variant of the language

• sentence_domain - the domain(s) the sentence belongs to

• source - the source the sentence was collected from

• is_used - whether the sentence is still in circulation for recording

• clips_count - number of clips recorded for this sentence

unvalidated_sentences.tsv

The unvalidated_sentences.tsv file contains one row per unvalidated sentence in the text corpus:

• sentence_id - unique identifier for the sentence

• sentence - the sentence text

• variant - the variant of the language

• sentence_domain - the domain(s) the sentence belongs to

• source - the source the sentence was collected from

• up_votes - number of upvotes the sentence received

• down_votes - number of downvotes the sentence received

• status - current status of the sentence (pending or rejected)

Get involved

Community links

• Common Voice translators on Pontoon

• Common Voice Communities

Discussions

• Common Voice on Matrix

• Common Voice on Discourse

• Common Voice on Discord

• Common Voice on Telegram

Contribute

https://commonvoice.mozilla.org/ewo/

• Speak

• Write

• Listen

• Review

Acknowledgements

Datasheet authors

Emmanuel Ngue Um , Ursule Sara Ngo Tjomb , Eliette Emilie-Caroline Ngo Tjomb Assembe

Citation guidelines

Ngué Um E, Ngo Tjomb EEC, Dibengue FL, Banum Manguele BM, Abo Djoulde B, Nyambe MA, Atangana Eloundou BM, Ngami Kamagoua JS, Mpouda Avom J, Nyobe Z, Eloundou Eyenga EG, Likwai AP (2025) Speech Technologies Datasets for African Under-Served Languages. Proceedings of the Eight Workshop on the Use of Computational Methods in the Study of Endangered Languages, edited by Lachler J, Agyapong G, Arppe A, Moeller S, Chaudhary A, Rijhwani S, Rosenblum D. URL Association for Computational Linguistics (ACL).

Funding

This dataset was partially funded by the Open Multilingual Speech Fund managed by Mozilla Common Voice.

Licence

This dataset is released under the Creative Commons Zero (CC-0) licence. By downloading this data you agree to not determine the identity of speakers in the dataset.

Footnotes

1. For a full list of age, gender, and accent options, see the demographics spec. These will only be reported if the speaker opted in to provide that information. ↩ ↩2 ↩3 ↩4