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Datasets:
mosesdaudu
/
switchboard-tierb-codeswitch

Tasks:
Automatic Speech Recognition
Modalities:
Audio
Formats:
soundfolder
Languages:
Nigerian Pidgin
Yoruba
Hausa
Size:
< 1K
Tags:
code-switching
african-languages
speech
telco
Libraries:
Datasets
License:
Dataset card Data Studio Files
xet
 Community
1
switchboard-tierb-codeswitch
File size: 4,969 Bytes
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---
license: cc-by-nc-sa-4.0
task_categories:
  - automatic-speech-recognition
language:
  - pcm
  - yo
  - ha
  - sw
  - en
tags:
  - code-switching
  - african-languages
  - speech
  - telco
pretty_name: SwitchBoard Tier B — African code-switched speech
size_categories:
  - n<1K
configs:
  - config_name: default
    data_files:
      - split: test
        path: data/*.wav
---

# 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.}
}
```