Sample Ghomala-TTS-Dataset

Sample-Ghomala-TTS-Dataset is a scripted speech dataset dedicated to the documentation and technological development of Ghomala (ISO 639-3: bbj), a Grassfields Bantu language spoken in the West Region of Cameroon. The dataset was compiled in the framework of the Mozilla Data Collective initiative (2026). The dataset comprises 997 high-quality audio recordings of Ghomala sentences read by a native speaker across 10 recording sessions (MP3 format), together with per-session sentence-to-audio mapping files enabling precise alignment between textual and acoustic data. Sentences were drawn from a scripted speech prompt list and read in a controlled environment. The transcription of all sentences follows the General Alphabet of Cameroon's Languages (AGLC; French acronym: Alphabet Général des Langues du Cameroun), the reference standard for Cameroonian national languages. The Ghomala orthography employed in this dataset is distinguished by an extended vowel inventory — including the schwa ə (mid central unrounded vowel), the open-mid front unrounded vowel ɛ, the open-mid back rounded vowel ɔ, and the high central rounded vowel ʉ (barred u), as well as the digraph aə functioning as a distinct complex vowel grapheme in Ghomala roots and affixes — alongside a rich consonant inventory comprising labio-velar stops (kp), labiodental obstruents (pf, bv), a velar fricative digraph (gh), postalveolar digraphs (sh, zh), affricates (c, ts, dz), labialized consonants (gw, gwy, cw), palatalised consonants (cy, shyə), and an extensive series of prenasalised and nasal-onset clusters (m+C and n+C, yielding forms such as mh, mn, mk, mgh, mf, mc, ms, mt, mzh, mj, mw, nt, nk, nw, nj, ny), as well as a multi-register tone-marking system combining level (acute, grave) and contour (caron, circumflex) diacritics applied to all nine vowels and to syllabic nasals, and the apostrophe (', U+0027) for marking glottal closure. The parallel availability of AGLC-transcribed text and aligned speech makes the dataset suitable for a wide range of applications, including text-to-speech (TTS) synthesis, automatic speech recognition (ASR), forced alignment, pronunciation modelling, and language learning tools. It also directly supports efforts to standardise and normalise the digital representation of Ghomala in language technology contexts.