mosesdaudu/switchboard-tierb-codeswitch

SwitchBoard Tier B — African code-switched speech

87 consented utterances of intra-sentential code-switching — Nigerian Pidgin, Yorùbá, Hausa and Kiswahili each mixed with English inside a single sentence — recorded from 8 bilingual volunteers at the Deep Learning Indaba 2026, Lagos.

Collected for the MLC (Africa) × Intron Agentic Voice AI Challenge as an evaluation set for telco/fintech voice agents. 8.75 minutes total.

What this is for

Measuring whether a speech model survives the switch points — and whether the errors it makes are the ones that change a transaction. Every clip carries per-token language tags, gold intent and slot labels, so you can compute PIER (error rate restricted to embedded-language tokens), Entity-WER and Numeric-WER, not just WER.

Composition

pair	clips	speakers
pcm-eng (Nigerian Pidgin × English)	30	2
yor-eng (Yorùbá × English)	28	2
swa-eng (Kiswahili × English)	20	2
hau-eng (Hausa × English)	9	2

77 clips Nigeria, 10 Kenya · 49 female, 38 male · 16 kHz mono WAV · 3.1–11.2 s (median 5.7 s) · 2 domains (telco 57, fintech 30) · 2 noise conditions (quiet 49, ambient 38).

Fields

file_name, transcription, speaker_id (pseudonymous SPK-xxxxxxxx), prompt_id, pair, language, duration_s, domain, intent, switch_type, accent, country, gender, age_range, noise_condition, device_type, slots (JSON), entities (JSON), lang_tags (space-separated, one tag per whitespace token of transcription).

lang_tags aligns 1:1 with transcription.split(). That alignment is enforced by the build script; a mismatch would shift the point-of-interest mask and make PIER measure the wrong tokens.

Limitations — stated as defects, not caveats

1. hau-eng is effectively one speaker. One volunteer completed 8 of 8 prompts; the second stopped after 1. Any Hausa-specific number rests on 9 clips.
2. No device diversity. All 87 clips are one USB headset. Real telco audio is 8 kHz narrowband over a handset; nothing here measures that.
3. Read speech, not conversation. Volunteers read prompts. No disfluency, barge-in, turn-taking or spontaneous repair.
4. 8 speakers, 2 per pair. Accent, device and noise floor are confounded with speaker identity. Block your bootstrap on speaker; it will not be narrow.
5. Geographic concentration. 77 of 87 clips from Nigeria.
6. Pidgin language tags are the least reproducible. Nigerian Pidgin is English-lexified, so the matrix/embedded boundary is a semantic judgement over orthographically English words.

Too small to train on, and too small to rank systems with. Use it to probe behaviour at switch points, not to declare a winner.

Consent and privacy

Every speaker gave explicit affirmative consent to this exact text, stored verbatim with a version and timestamp alongside their record:

"I have read the information about this research recording. I agree to my voice recordings and their transcripts being published under CC BY-NC-SA 4.0 for research on African speech recognition. I understand no personal information is spoken, and that I may have my recordings deleted at any time by contacting the researcher."

Prompts contain no real personal data — the names and phone numbers spoken were written for the script, and no volunteer spoke their own name, number or any account detail. Speaker identifiers are pseudonymous; the name↔id mapping is held offline solely so a deletion request can be honoured, and has never been published.

One correction, found by audit. We previously described the spoken names as "invented". One is not: prompt yor-010 uses "Adebayo Ogunlesi", the name of a real and well-known Nigerian businessman. Both parts are ordinary Yorùbá names and the collision was accidental — the prompt is a routine "my name is X, my number is Y" customer-service line and says nothing about that person — but "invented" was the wrong word. The audio is already recorded and consented, so it has not been altered; the name is flagged here and a re-record should replace it.

Withdrawal

Any speaker may have their clips removed. Contact the maintainer; clips are deleted and the release id is incremented.

Citation

@misc{switchboard_tierb_2026,
  title  = {SwitchBoard Tier B: African code-switched speech for voice-agent evaluation},
  author = {Daudu, Moses},
  year   = {2026},
  note   = {Deep Learning Indaba 2026, Lagos. CC BY-NC-SA 4.0.}
}