Code-Switching ASR · ગુજરાતી
Gujarati Data for Code-Switching ASR
Recognising speech that switches between an Indian language and English several times per sentence. In Gujarati, the binding constraint is usually dialect coverage and code-mixing, not raw hours.
- Language
- Gujarati
- Primary metric
- Switch-point accuracy
- Typical volume
- 250-1,000 hours
Data profile required
- Genuinely code-mixed spontaneous speech
- Per-token language ID labels
- A fixed rule for script of English tokens
What Gujarati adds to the requirement
- Murmured (breathy-voiced) vowels are phonemic in Gujarati and are absent from most shared Indic acoustic models
- Surti speech has distinctive intonation and vowel quality that mismatches Ahmedabad-trained models
- Dialects to cover: Standard (Amdavadi), Surti, Kathiyawadi, Kachchhi-influenced
- Business and trade vocabulary is heavily English; Gujarati diaspora speech adds further English structure. Specify whether diaspora speakers are in or out of scope.
Metrics to track
- Switch-point accuracy
- Mixed-utterance WER
- Language ID token accuracy
Failure modes
- Concatenating monolingual data and calling it code-mixed
- Leaving script conventions to individual annotators
For Gujarati specifically: Very little spontaneous Gujarati audio exists publicly; nearly all of it is Ahmedabad read speech.
Recommended cohort
Surat and Rajkot recruitment is essential for dialect coverage; Ahmedabad-only cohorts sound uniform.
| Dimension | Typical split | Why it matters for Gujarati |
|---|---|---|
| Gender | 50 / 50 | Pitch range differences change acoustic model behaviour; unbalanced cohorts bias recognition |
| Age | 18-25: 30%, 26-40: 40%, 41-60: 30% | Older speakers retain conservative Gujarati forms that younger urban speakers have lost |
| Region | Gujarat / Daman & Diu / Dadra & Nagar Haveli and others | Dialect spread across 5 recognised varieties |
| Education | Mixed, including below-graduate | Prompt-reading fluency correlates with education and skews prosody |
| Condition | Studio / quiet room / field | Match the noise profile of your deployment |
Suggested programme shape
Start with an evaluation set of 100 speakers spread across every Gujarati dialect in scope, collected before training data. Then field 250-1,000 hours of training data from disjoint speakers.
This ordering is what makes the improvement measurable rather than assumed.
Frequently asked
Is there usable public Gujarati data for code-switching asr?
Very little spontaneous Gujarati audio exists publicly; nearly all of it is Ahmedabad read speech.
How many Gujarati speakers do we need?
500-1,500 speakers for a training corpus, plus a disjoint evaluation cohort covering each dialect. Speaker count matters more than hours for generalisation.
Can you run this across multiple languages at once?
Yes. Multi-language programmes run to one master specification so per-language results stay comparable.
Scope Gujarati data for code-switching asr
Send the target metric and the languages in scope.