---
pretty_name: IndicTelephony-Bench
language:
- bn
- en
- gu
- hi
- kn
- ml
- mr
- ta
- te
license: cc-by-4.0
task_categories:
- automatic-speech-recognition
tags:
- speech
- stt
- asr
- telephony
- code-switching
- indian-languages
- benchmark
- 8khz
size_categories:
- 10K<n<100K
configs:
- config_name: all
  default: true
  data_files:
  - split: test
    path: "*/test-*"
- config_name: Bengali
  data_files:
  - split: test
    path: Bengali/test-*
- config_name: English
  data_files:
  - split: test
    path: English/test-*
- config_name: Gujarati
  data_files:
  - split: test
    path: Gujarati/test-*
- config_name: Hindi
  data_files:
  - split: test
    path: Hindi/test-*
- config_name: Kannada
  data_files:
  - split: test
    path: Kannada/test-*
- config_name: Malayalam
  data_files:
  - split: test
    path: Malayalam/test-*
- config_name: Marathi
  data_files:
  - split: test
    path: Marathi/test-*
- config_name: Tamil
  data_files:
  - split: test
    path: Tamil/test-*
- config_name: Telugu
  data_files:
  - split: test
    path: Telugu/test-*
---

# IndicTelephony-Bench v1.0

An open benchmark for speech recognition on **Indian telephone speech**: 25,393
human-curated utterances (30.2 hours) in nine languages, recorded
over live phone lines and released exactly as the line delivered them, at 8 kHz.
Most of it is code-mixed, much of it is short, and the business vocabulary a voice
agent acts on is tagged in the references.

## At a glance

| | |
|---|---|
| Utterances | 25,393 |
| Audio | 30.18 hours, 8 kHz mono 16-bit PCM WAV, embedded in the parquet shards |
| Files | one parquet shard per language, under `<Language>/test-*.parquet` |
| Calls | 320 |
| Languages | Bengali, English, Gujarati, Hindi, Kannada, Malayalam, Marathi, Tamil, Telugu |
| Code-mixed utterances | 70.4% |
| Shorter than 2 s | 30.0% |
| Median duration | 3.10 s |
| Utterances with tagged keywords | 66.0% |
| Split | `test` only: this is an evaluation set, not training data |

## Languages

| Language | Code | Utterances | Hours | Calls | Code-mixed % | < 2 s % | Median s |
|---|---|---|---|---|---|---|---|
| Bengali | `bn` | 2,211 | 3.25 | 40 | 90.9 | 6.9 | 4.20 |
| English | `en` | 3,672 | 3.01 | 164 | – | 63.8 | 1.42 |
| Gujarati | `gu` | 2,230 | 3.90 | 54 | 87.6 | 10.1 | 5.16 |
| Hindi | `hi` | 2,837 | 3.35 | 89 | 76.1 | 41.8 | 2.76 |
| Kannada | `kn` | 3,549 | 3.92 | 85 | 79.4 | 27.2 | 2.66 |
| Malayalam | `ml` | 2,319 | 3.29 | 20 | 92.1 | 8.9 | 4.08 |
| Marathi | `mr` | 2,111 | 3.43 | 34 | 97.8 | 3.7 | 5.22 |
| Tamil | `ta` | 3,173 | 3.00 | 56 | 86.1 | 36.4 | 2.44 |
| Telugu | `te` | 3,291 | 3.02 | 76 | 60.7 | 40.0 | 2.34 |

Each language was recorded on a small number of lines, so compare systems within a
language rather than languages with each other.

## Loading

```python
from datasets import load_dataset

ds = load_dataset("ConvoZenAI/indictelephony-bench", split="test")               # every language
ta = load_dataset("ConvoZenAI/indictelephony-bench", "Tamil", split="test")      # one language

row = ds[0]
row["audio"]          # {"array": ..., "sampling_rate": 8000, "path": ...}
row["transcription"]
```

The audio is 8 kHz because that is what a telephone line carries. If a model needs
16 kHz, resample at load time, for example
`ds.cast_column("audio", Audio(sampling_rate=16000))`, and keep in mind that
upsampling adds no information above 4 kHz.

## Data fields

| Field | Description |
|---|---|
| `audio` | the utterance, 8 kHz mono 16-bit PCM |
| `utterance_id` | stable identifier, e.g. `call_0001_chunk0014` |
| `call_id` | the call the utterance comes from; use it to group utterances, for example for call-level bootstrap intervals |
| `language` | base language code (`bn`, `en`, `gu`, `hi`, `kn`, `ml`, `mr`, `ta`, `te`) |
| `language_tag` | every language present, e.g. `en-hi` for Hindi mixed with English |
| `is_code_mixed` | true when more than one language is present |
| `duration_sec` | duration in seconds |
| `domain` | business scenario of the turn (see below) |
| `transcription` | the curated reference transcript |
| `transcription_normalized` | the same transcript after the released scoring normalizer: NFKC, lower-cased, apostrophes dropped, other punctuation to space, numbers written as digit sequences, and a short map of alternative spellings of the same word. This is the string WER and CER are actually computed against. |
| `keywords` | comma-separated domain-salient spans of the transcript (amounts, product names, places); empty if none |

## Transcription convention

- English words in Latin script; words of the Indian language in their native script.
- No translation and no transliteration.
- Numbers written as spoken.
- Fillers kept.
- No punctuation.

Score against the reference only after applying the released normalizer to both
the reference and the hypothesis (see *Evaluation*). Raw string comparison charges
systems for casing, punctuation and number formatting that are not recognition
errors. For WER and CER the `transcription_normalized` column already carries the
reference in that form, so you only have to normalize your own output; the
normalizer is released, so the two sides are treated identically:
<https://github.com/ConvoZenAI/indictelephony-bench>. The `keywords` column is stored as curated, so normalize it yourself
before computing the keyword miss rate.

## Domains

| Domain | Value | Hours | Share of hours % | Utterances |
|---|---|---|---|---|
| Real estate | `real_estate` | 7.77 | 25.7 | 5,404 |
| Automotive | `automotive` | 6.18 | 20.5 | 5,567 |
| Other | `other` | 3.98 | 13.2 | 4,267 |
| Banking & finance | `banking_finance` | 3.04 | 10.1 | 2,760 |
| Insurance | `insurance` | 2.74 | 9.1 | 1,878 |
| Telecom | `telecom` | 2.58 | 8.6 | 1,887 |
| E-commerce | `e_commerce` | 1.89 | 6.2 | 1,864 |
| Education | `education` | 1.15 | 3.8 | 1,058 |
| Healthcare | `healthcare` | 0.85 | 2.8 | 708 |

Shares are of audio, not of rows: median turn length varies about twofold between
domains, so a domain can hold more utterances and less speech than another.

## How it was built

- **Recording.** Pairs of speakers hold a call over a live SIP telephone
  connection, working through turns scripted across business scenarios. The turns
  are spoken to another person rather than read aloud alone at a microphone, so
  the pauses and backchannels are real; the channel, codec, band limit and turn
  timing are real too. Only the wording is prepared.
- **Segmentation.** Calls were trimmed of ring and IVR lead and split into
  single-speaker turns.
- **Curation.** Curators transcribed each turn while listening to the audio, with
  machine transcripts from several systems shown as optional drafts, system
  identities hidden, and instructions that the audio takes precedence over any
  draft. Curators also assigned the language tag, the domain and the keywords.
- **Filtering.** Utterances flagged as containing personal information were removed
  together with their audio, along with empty and unintelligible turns.

## Evaluation

Scoring code: <https://github.com/ConvoZenAI/indictelephony-bench> (Apache 2.0) --
the normalizer, all five metrics and the call-level bootstrap.

Every metric is computed after one normalizer, applied identically to the reference
and to every system's output: NFKC and lowercase, apostrophes removed, other
punctuation to space, numbers canonicalised so spoken and written forms compare
equal, a short explicit list of alternative spellings of the same word, and filler
spellings collapsed. It deliberately does not transliterate between scripts, which
would merge genuinely different graphemes and forgive real errors.
`transcription_normalized` is the reference after that step, so scores can be
reproduced without reimplementing it.

The reference metrics are WER, CER, a mixed error rate for code-mixed text (English
tokens as words, Indian-language tokens as aksharas), a keyword miss rate over the
tagged keywords (one minus keyword recall, with no insertion term), and a semantic
WER that forgives only verified same-word differences. Rates are corpus-level (total errors over total reference units). Score
an utterance a system returned nothing for as fully deleted, and exclude an
utterance from a system's denominator only when the system does not support one of
its languages.

## Limitations

- **Scripted speech.** Results describe telephone-band recognition of business
  vocabulary, not spontaneous conversation.
- **Clean recordings.** The benchmark tests band limitation and code-mixing, not
  robustness to background noise.
- **Few lines per language.** Speaker and language are confounded, so differences
  between languages are not meaningful.
- **One reference per utterance.** The normalizer and semantic WER absorb
  systematic variation, but spelling variants outside the normalizer's list still
  count as errors under WER and CER.
- **Draft-assisted curation.** Curators saw machine drafts, including from a system
  developed by the dataset authors. Drafts were blinded and several were shown.

## Licence

Released under [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/)
(CC BY 4.0). You may share and adapt the data, including commercially, provided you
give credit, link to the licence, and state whether you changed anything. The
citation below satisfies the attribution requirement.

The licence covers copyright. It does not grant rights over the speakers' personal
data, and it comes with no warranty: the references are human work and may contain
errors.

**Ethical use.** The recordings are of consenting speakers. We ask that you do not
attempt to identify them, and that you do not use the audio to clone or synthesise
their voices. This is a request, not a licence condition.

## Citation

```bibtex
@misc{indictelephonybench2026,
  title        = {IndicTelephony-Bench: An Open ASR Benchmark for Indian Telephone Speech},
  author       = {Priyadarshi, Prasoon and Abdul Azeez, Zaheer and {ConvoZen AI}},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/ConvoZenAI/indictelephony-bench}},
  note         = {25,393 utterances, 30.18 hours, nine languages, 8 kHz telephony}
}
```