Code-Switching ASR · मराठी
Marathi Data for Code-Switching ASR
Recognising speech that switches between an Indian language and English several times per sentence. In Marathi, the binding constraint is usually dialect coverage and code-mixing, not raw hours.
- Language
- Marathi
- Primary metric
- Switch-point accuracy
- Typical volume
- 500-2,000 hours
Data profile required
- Genuinely code-mixed spontaneous speech
- Per-token language ID labels
- A fixed rule for script of English tokens
What Marathi adds to the requirement
- Retains the retroflex lateral ळ, which has no Hindi or English equivalent and is frequently substituted with ल by non-native transcribers
- Affricates च and ज have both alveolar and palatal realisations depending on the word, a distinction lost in Devanagari orthography
- Dialects to cover: Standard (Puneri), Varhadi (Vidarbha), Marathwadi, Konkani-influenced coastal Marathi
- Mumbai and Pune speech mixes Marathi, Hindi, and English in the same sentence. Marathi-only recordings collected in Pune under-represent the Mumbai reality of tri-lingual switching.
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 Marathi specifically: Available Marathi speech data is dominated by standard Puneri read speech. Vidarbha, Marathwada, and coastal Konkan varieties are severely under-collected, which is exactly where deployed voice products lose accuracy.
Recommended cohort
A representative Marathi cohort should be split roughly 40% western Maharashtra, 25% Vidarbha, 20% Marathwada, 15% Konkan rather than concentrated in Pune.
| Dimension | Typical split | Why it matters for Marathi |
|---|---|---|
| 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 Marathi forms that younger urban speakers have lost |
| Region | Maharashtra / Goa / parts of Karnataka and others | Dialect spread across 6 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 120 speakers spread across every Marathi dialect in scope, collected before training data. Then field 500-2,000 hours of training data from disjoint speakers.
This ordering is what makes the improvement measurable rather than assumed.
Frequently asked
Is there usable public Marathi data for code-switching asr?
Available Marathi speech data is dominated by standard Puneri read speech. Vidarbha, Marathwada, and coastal Konkan varieties are severely under-collected, which is exactly where deployed voice products lose accuracy.
How many Marathi speakers do we need?
1,000-3,000 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 Marathi data for code-switching asr
Send the target metric and the languages in scope.