Native-speaker language quality for AI systems
Speech Evaluation • Annotation • Localization • Conversational AI QA
Native Hausa speaker with professional experience creating and processing Hausa-language data for machine-translation research, combined with modern experience evaluating conversational AI, localization quality, speech and transcription output, and culturally appropriate Hausa language.
About
I am a native Hausa speaker and AI practitioner with professional experience creating, reviewing, translating, annotating, and organizing Hausa-language data for machine-translation research.
At CACI International, I worked on an Electronic Hausa Corpus used for machine-translation research. My responsibilities included Hausa document collection, translation, metadata classification, script-encoding review, entity tagging, corpus processing, and secure dataset delivery.
Today, I apply that experience to modern conversational AI — reviewing Hausa language for naturalness, grammar, contextual accuracy, terminology consistency, gender-sensitive wording, code-switching, cultural appropriateness, and native-speaker usability.
My Linux and AI systems background allows me to work comfortably with engineers, researchers, annotation teams, and production AI workflows.
Hausa AI Evaluation
I evaluate Hausa conversational AI by asking more than whether a translation is technically understandable. I review whether a native speaker would naturally use or accept the wording in the intended social and conversational context.
Ba a sani ba
Ban tabbatar ba
Annotation rationale “Ba a sani ba” is impersonal and can feel blunt in this conversational context. “Ban tabbatar ba” more naturally communicates that the speaker is personally uncertain.
Tsallake yanzu
Skip
Annotation rationale For the target WhatsApp audience, “Skip” is already a familiar interface term. A forced literal Hausa translation sounds less natural and can make the interaction harder rather than easier.
Using “ɗan” for a female family member.
Use “ɗiyar” for a female subject.
Annotation rationale Hausa kinship terminology must agree with the gender and relationship of the person being discussed.
“Would a native Hausa speaker naturally say or understand this in the intended situation?”
— Evaluation principle
Speech & Transcription
This portfolio includes native-speaker comparison between machine-generated speech transcripts and manually verified Hausa reference transcripts — identifying not only what a model got wrong, but why it failed.
60–90 seconds of natural Hausa speech recorded by a native speaker.
Audio unavailable — the sample file could not be loaded.
A barkarmu da yamma, sunana Jiban Saulawa. Ah, ni Bahaushe ne amma mazaunin ƙasar Amurka. Ni ɗan asalin jihar Katsina ne. Ah, kuma na yi makarantar sakandare, wato boarding school, a Kano. Saboda haka, na saba da bambance-bambancen karin harshen Hausa daga wurare daban-daban, saboda mun zauna tare da mutane daga ƙasar Yobe, ah Kaduna, Sokoto, ah Maiduguri, waɗanda harshen Hausarsu daban-daban ne da tawa irin ta Katsina. Alal misali, kamar ɗan Sokoto idan ya ce 'ina kwana'—wato kamar mu sai dai mu ce 'ina barci', ba 'ina kwana' ba. Kuma mu in ka ce 'ina kwana', wata kalma ce daban, kamar kana cewa 'good morning' kenan. Ah saboda haka, wannan ya taimake ni sosai. Wannan yana da muhimmanci sosai wajen AI language evaluation, domin wani abu zai iya zama abin fahimta amma bai yi natural ba ga wani yanki ko wani irin mai magana. A yanzu ina aiki da conversational AI da localization a cikin wani WhatsApp platform da nake haɗawa ko nake ginawa. A lokacin gwaji na lura cewa AI zai iya ba da fassara mai ma'ana amma ba lallai ta zama irin Hausar da mutane suke amfani da ita a zahiri ba. Misali, an taɓa ba ni, an taɓa amfani da 'tsallake yanzu' a matsayin fassarar 'skip'. Ah nahawance ana iya fahimta, amma a WhatsApp interface kalmar 'skip' kanta ta fi sauƙi kuma mutane sun saba da ita, kuma za su fahimta haka. Akwai ire-iren misali da yawa irin wannan kamar da na gani ina ta gyara ma AI su domin ci gaba da wannan abu. Haka kuma akwai bambanci tsakanin 'ba a sani ba' da 'ban tabbatar ba'. Idan mutum yana nuna rashin tabbaci, ah 'ban tabbatar ba' ya fi dacewa da context ɗin conversation. Wani misali kuma shi ne lokacin da AI ya kawo 'chai', that's 'godowo'. Wannan na iya zama sanannen salon magana a wasu sassan Najeriya, amma ba ya nufin cewa Bahaushe zai yi amfani da shi a wannan yanayin ba. Wannan shi ne inda native speaker review yake da muhimmanci. Ba wai kawai a duba ko fassarar ta yi daidai ba, amma a duba naturalness, context, dialect, da cultural fit.
Raw model output (Gemini, from the recording above) — reproduced unmodified, including transcription errors. These errors are the evidence reviewed in the annotation below.
Ah barkanmu da yamma, sunana Jiban Saulawa. Ah, ni Bahaushe ne amma mazaunin ƙasar Amurka. Ni ɗan asalin jahar Katsina ne. Ah, kuma na yi makarantar sakandare, wato boarding school, a Kano. Saboda haka, na saba da bambance-bambancen karin harshen Hausa daga wurare daban-daban, saboda mun zauna tare da mutane daga ƙasar Yobe, ah Kaduna, Sokoto, ah Maiduguri, waɗanda harshen Hausassu daban-daban ne da tawa irin ta Katsina.
Alal misali, kamar ɗan Sokoto idan ya ce 'ina kwana'—wato kamar mu sai dai mu ce 'ina bacci', ba 'ina kwana' ba. Kuma mu in ka ce 'ina kwana', wata kalma ce daban, kamar kana cewa 'good morning' kenan. Ah saboda haka, wannan ya taimake ni sosai. Wannan yana da muhimmanci sosai wajen AI language evaluation, domin wani abu zai iya zama abin fahimta amma bai yi natural ba ga wani yanki ko wani irin mai magana.
A yanzu ina aiki da conversational AI da localization a cikin wani WhatsApp platform da nake haɗawa ko nake ginawa. A lokacin gwaji na lura cewa AI zai iya ba da fassara mai ma'ana amma ba lallai ta zama irin Hausar da mutane suke amfani da ita a zahiri ba. Misali, an taɓa ba ni, an taɓa amfani da 'tsallake yanzu' a matsayin fassarar 'skip'. Ah nahawance ana iya fahimta, amma a WhatsApp interface kalmar 'skip' kanta ta fi sauƙi kuma mutane sun saba da ita, kuma za su fahimta haka. Akwai ire-iren misali da yawa irin wannan kamar da na gani ina ta gyara ma AI su domin ci gaba da wannan abu.
Haka kuma akwai bambanci tsakanin 'ba a sani ba' da 'ban tabbatar ba'. Idan mutum yana nuna rashin tabbaci, ah 'ban tabbatar ba' ya fi dacewa da context ɗin conversation. Wani misali kuma shi ne lokacin da AI ya kawo 'chai', there is God oo'. Wannan na iya zama sanannen salan magana a wasu sassan Najeriya, amma ba ya nufin cewa Bahaushe zai yi amfani da shi a wannan yanayin ba.
Wannan shi ne inda native speaker review yake da muhimmanci. Ba wai kawai a duba ko fassarar ta yi daidai ba, amma a duba naturalness, context, dialect, da cultural fit.
Manually transcribed and verified by the native speaker — the ground-truth reference for the annotation below. Reproduced verbatim, including fillers, code-switching, and dialect forms.
Native-speaker review of the raw model output against the reference transcript. The goal is not an error count — it is showing the kind of language judgment each difference requires.
Model “A barkarmu da yamma” → Gold “Ah barkanmu da yamma”
Error type: Lexical / phrase recognition
The model misrecognized the opening greeting and omitted part of the spoken form. In verbatim transcription, the actual spoken wording must be preserved even when the model output is plausible Hausa.
Model “ina barci” → Gold “ina bacci”
Error type: Dialect-sensitive lexical substitution
The model substituted a different Hausa lexical form from the one actually spoken. This matters especially here, because the speaker was discussing regional Hausa variation. A transcript must preserve the speaker’s lexical choice rather than normalize it to another understandable form.
Model “chai', that's 'godowo'” → Gold “chai', there is God oo'”
Error type: Code-switching / phrase recognition
The model distorted a code-switched Nigerian English/Pidgin expression, changing the example being discussed — evidence of difficulty with mixed-language speech.
Model “jihar Katsina” → Gold “jahar Katsina”
Error type: Normalization / spoken-form preservation
For a strict verbatim transcript, the reference should preserve what the speaker actually said rather than silently normalize the form.
Model “salon magana” → Gold “salan magana”
Error type: Phonetic / normalization difference
The model normalized the spoken realization. Whether this counts as an error depends on the transcription guideline — itself an annotation-policy decision.
Model “harshen Hausarsu” → Gold “harshen Hausassu”
Error type: Spoken-form normalization
The model regularized the spoken form instead of preserving the speaker’s actual realization — which is why transcription tasks must define whether they require verbatim or normalized orthography.
Examples 4–6 are transcription-policy decisions: they hinge on whether the guideline requires strict verbatim or normalized orthography. Examples 1–3 are errors under any guideline. In every case the model produced fluent, plausible Hausa — which is exactly why native-speaker review is required for plausible-but-contextually-wrong output.
The goal is not only to identify incorrect words, but to determine why the model failed and whether the resulting transcript preserves the speaker’s intended meaning.
Worlds Apart Localization
Worlds Apart Comedy is an AI-assisted comedy series built around cultural contrasts and family life. The series is primarily produced in English.
Hausa localization and language review are performed directly by me as a native speaker. Yoruba and Igbo content is reviewed with native-speaker collaborators.
WhatsApp Demo
This live demonstration shows native-speaker-reviewed Hausa conversational prompts, family and relationship terminology, contextual language behavior, and localized WhatsApp interactions.
Connects to the LogicGrid demo line (+234 812 277 7132) — a business demonstration number, not a personal contact.
Resume
My experience combines Hausa machine-translation corpus work, current conversational AI evaluation, localization, and more than a decade of Linux and infrastructure engineering.
Hausa Electronic Corpus / Machine Translation Research
Infrastructure, security-conscious systems, and production operations
Hausa language evaluation and native-speaker review for AI systems
Supported development of an Electronic Hausa Corpus for machine-translation research.
A secondary but practical strength: I can work directly inside the tooling and pipelines around language data.
As evidence of this experience, I built SafeRelay — a personal project demonstrating production AI workflow automation with structured data handling and privacy safeguards, which is practical context for working inside language-data pipelines.
Contact
Available for Hausa AI language evaluation, transcription review, localization, annotation-quality work, speech evaluation, and related language-data projects.
Jiban Saulawa