---
pretty_name: "Indian-language calls between people and an AI voice agent"
language:
- as
- en
- gu
- hi
- kn
- ml
- mr
- or
- pa
- te
- ur
language_details: as-IN, en-IN, gu-IN, hi-IN, kn-IN, ml-IN, mr-IN, or-IN, pa-IN, te-IN, ur-IN
license: other
license_name: lokah-custom
license_link: https://kenpathlabs.com/lokah/licensing
task_categories:
- audio-to-audio
- automatic-speech-recognition
size_categories:
- n<1K
tags:
- Assamese speech dataset
- Indian English speech dataset
- Gujarati speech dataset
- Hindi speech dataset
- Kannada speech dataset
- Malayalam speech dataset
---

# Indian-language calls between people and an AI voice agent

> Phone calls between a person and an AI voice agent in eleven Indian languages, on recruitment, telecom, healthcare, e-commerce and insurance, each voice on its own channel at 48 kHz. A sample release: 22 calls, 83 minutes, with verbatim transcripts of both sides. Recorded to order in your languages, domains and scenarios, at any volume.

`LK-SP-MUL-001` · [Get a quote](https://kenpathlabs.com/lokah/datasets/ai-voice-agent-calls#contact) · [Record as JSON](https://kenpathlabs.com/api/lokah/datasets/ai-voice-agent-calls) · [Croissant](https://kenpathlabs.com/lokah/datasets/ai-voice-agent-calls/croissant.json)

Phone calls between a person and an AI voice agent in eleven Indian languages, on recruitment, telecom, healthcare, e-commerce and insurance, each voice on its own channel at 48 kHz. A sample release: 22 calls, 83 minutes, with verbatim transcripts of both sides. Recorded to order in your languages, domains and scenarios, at any volume. Measured from 22 sample conversations (83 minutes): 48 kHz, 16-bit FLAC, one two-channel file per conversation. Across the whole set 18% of transcript words are English written in Latin script and 23% of the time is silence; the sample conversations hold 544 turns. 17 callers (5 male, 12 female) and one AI voice agent (female voice). Every recording has a complete, segment-level transcript. Personal data: redacted. Licence: custom, quoted per use.

## Specification

| Field | Value | Note |
| --- | --- | --- |
| id | LK-SP-MUL-001 |  |
| type | speech · conversational · call centre |  |
| language | অসমীয়া · Assamese · as-IN / English · Indian English · en-IN / ગુજરાતી · Gujarati · gu-IN / हिन्दी · Hindi · hi-IN / ಕನ್ನಡ · Kannada · kn-IN / മലയാളം · Malayalam · ml-IN / मराठी · Marathi · mr-IN / ଓଡ଼ିଆ · Odia · or-IN / ਪੰਜਾਬੀ · Punjabi · pa-IN / తెలుగు · Telugu · te-IN / اردو · Urdu · ur-IN |  |
| hours | 1 h |  |
| channels | Two channels, one per speaker |  |
| files | 22 files · 22 conversations | counted across the sample release |
| layouts | 22 two-channel | counted across the sample release |
| audio | FLAC · 48 kHz · 16-bit | measured across the sample release |
| bandwidth | super-wideband (16 kHz) |  |
| snr | 34.4 dB median | across the sample release, on the caller's channel |
| release | sample release | the full collection is recorded to order |
| transcript | time-aligned by segment · Bengali-Assamese and Latin and Gujarati and Devanagari and Kannada and Malayalam and Odia and Gurmukhi and Telugu and Perso-Arabic script · delivered as JSON |  |
| speakers | 17 callers and one AI voice agent (female synthetic voice) |  |
| pii | Numbers of 4 or more digits, digit strings spoken as words, and email addresses are masked as [pii] in text and replaced by a tone in audio. First names are not masked. |  |
| source | Recorded to order with an AI voice agent |  |
| review | Every recording has a complete, segment-level transcript |  |
| personal data | Redacted |  |
| licence | Custom | quoted per use |


## What it is good for

| Task | Fit | Why |
| --- | --- | --- |
| Speech recognition | yes | Time-aligned transcripts in native script, English kept as spoken. 34.4 dB median SNR across the full set, wideband (39.5 dB in the sample conversations). |
| Full-duplex speech to speech | yes | Each speaker on a separate channel, so overlap, backchannels and turn timing survive. Moshi and PersonaPlex train on exactly this layout. |
| Turn-taking and endpointing | yes | Gaps and overlaps at every change of speaker are measured from the two channels; see the profile. |
| Voice agents for support | yes | Caller and agent turns across support and recruitment scenarios, with the agent on its own channel. |
| Speaker diarisation | yes | Speaker-attributed segments across full conversations. |
| Text to speech | partly | 34.4 dB median SNR across the full set, wideband (39.5 dB in the sample conversations): clean and wideband enough for conversational prosody data, though not a studio voice. |

## Conversation profile

Across every conversation in the dataset (22 conversations, median 3.75 minutes), as measured by the delivery.

| Measure | Value |
| --- | --- |
| Talk time, most-talking speaker / the other | 59% / 41% |
| Silence | 23% |
| Overlapping speech | 2% |
| English words, written in Latin script | 18% |
| Turns a minute | 6.12 |
| Speaking rate | 122.8 words a minute |

### Turn-taking, measured from the two channels across the set

| Per minute | This dataset | Fisher corpus |
| --- | --- | --- |
| Inter-pausal units | 18.07 | 21.6 |
| Pauses | 8.88 | 7 |
| Gaps | 5.86 | 7.5 |
| Overlaps | 0.95 | 6.5 |
| Backchannels | 1.94 | not reported |

Floor-transfer offset: median +1.35 s, 10th to 90th percentile -0.29 s to +2.26 s. Channel isolation: 60 dB.


## Audio quality

Frame RMS at 20 ms on the channels mixed to mono. Noise floor is the 10th percentile, speech level the 90th; SNR is their difference. Bandwidth is the highest frequency at which speech still rises 6 dB above the recording's own noise spectrum.

| Measure | Value | Reading |
| --- | --- | --- |
| SNR across every file of the set | 34.4 dB median | caller's channel |
| Speech above noise floor (SNR), sample conversations | 39.5 dB (20.7 to 62.9) | clean |
| Noise floor | -59 dBFS |  |
| Effective bandwidth | 8.0 kHz | super-wideband (16 kHz) across the full set |
| Clipping | 0.002% of samples |  |
| DNSMOS P.835 (1 to 5) | background 3.92, speech 3.5, overall 3.16 | listener-rated quality, estimated |

## Samples

22 public excerpts, 43 to 336 seconds each, from different conversations in the dataset.

- Sample 1, Recruitment: phone screening (Marathi), from a 5-minute conversation: [audio](https://kenpathlabs.com/lokah-samples/ai-voice-agent-calls--1.m4a) · [segments](https://kenpathlabs.com/lokah-samples/ai-voice-agent-calls--1.segments.json)
- Sample 2, Customer service: mobile network complaint (Punjabi), from a 4-minute conversation: [audio](https://kenpathlabs.com/lokah-samples/ai-voice-agent-calls--2.m4a) · [segments](https://kenpathlabs.com/lokah-samples/ai-voice-agent-calls--2.segments.json)
- Sample 3, Customer service: mobile network complaint (Hindi), from a 3-minute conversation: [audio](https://kenpathlabs.com/lokah-samples/ai-voice-agent-calls--3.m4a) · [segments](https://kenpathlabs.com/lokah-samples/ai-voice-agent-calls--3.segments.json)
- Sample 4, Customer service: mobile network complaint (Odia), from a 4-minute conversation: [audio](https://kenpathlabs.com/lokah-samples/ai-voice-agent-calls--4.m4a) · [segments](https://kenpathlabs.com/lokah-samples/ai-voice-agent-calls--4.segments.json)
- Sample 5, Recruitment: phone screening (Indian English), from a 3-minute conversation: [audio](https://kenpathlabs.com/lokah-samples/ai-voice-agent-calls--5.m4a) · [segments](https://kenpathlabs.com/lokah-samples/ai-voice-agent-calls--5.segments.json)
- Sample 6, Customer service: clinic appointment booking (Gujarati), from a 4-minute conversation: [audio](https://kenpathlabs.com/lokah-samples/ai-voice-agent-calls--6.m4a) · [segments](https://kenpathlabs.com/lokah-samples/ai-voice-agent-calls--6.segments.json)
- Sample 7, Recruitment: phone screening (Kannada), from a 4-minute conversation: [audio](https://kenpathlabs.com/lokah-samples/ai-voice-agent-calls--7.m4a) · [segments](https://kenpathlabs.com/lokah-samples/ai-voice-agent-calls--7.segments.json)
- Sample 8, Recruitment: customer-support role interview (Telugu), from a 6-minute conversation: [audio](https://kenpathlabs.com/lokah-samples/ai-voice-agent-calls--8.m4a) · [segments](https://kenpathlabs.com/lokah-samples/ai-voice-agent-calls--8.segments.json)
- Sample 9, Customer service: e-commerce return and refund (Malayalam), from a 5-minute conversation: [audio](https://kenpathlabs.com/lokah-samples/ai-voice-agent-calls--9.m4a) · [segments](https://kenpathlabs.com/lokah-samples/ai-voice-agent-calls--9.segments.json)
- Sample 10, Recruitment: customer-support role interview (Assamese), from a 5-minute conversation: [audio](https://kenpathlabs.com/lokah-samples/ai-voice-agent-calls--10.m4a) · [segments](https://kenpathlabs.com/lokah-samples/ai-voice-agent-calls--10.segments.json)
- Sample 11, Customer service: two-wheeler insurance claim (Urdu), from a 2-minute conversation: [audio](https://kenpathlabs.com/lokah-samples/ai-voice-agent-calls--11.m4a) · [segments](https://kenpathlabs.com/lokah-samples/ai-voice-agent-calls--11.segments.json)
- Sample 12, Recruitment: phone screening (Telugu), from a 4-minute conversation: [audio](https://kenpathlabs.com/lokah-samples/ai-voice-agent-calls--12.m4a) · [segments](https://kenpathlabs.com/lokah-samples/ai-voice-agent-calls--12.segments.json)
- Sample 13, Customer service: clinic appointment booking (Kannada), from a 4-minute conversation: [audio](https://kenpathlabs.com/lokah-samples/ai-voice-agent-calls--13.m4a) · [segments](https://kenpathlabs.com/lokah-samples/ai-voice-agent-calls--13.segments.json)
- Sample 14, Customer service: clinic appointment booking (Assamese), from a 3-minute conversation: [audio](https://kenpathlabs.com/lokah-samples/ai-voice-agent-calls--14.m4a) · [segments](https://kenpathlabs.com/lokah-samples/ai-voice-agent-calls--14.segments.json)
- Sample 15, Customer service: mobile network complaint (Assamese), from a 4-minute conversation: [audio](https://kenpathlabs.com/lokah-samples/ai-voice-agent-calls--15.m4a) · [segments](https://kenpathlabs.com/lokah-samples/ai-voice-agent-calls--15.segments.json)
- Sample 16, Recruitment: customer-support role interview (Marathi), from a 4-minute conversation: [audio](https://kenpathlabs.com/lokah-samples/ai-voice-agent-calls--16.m4a) · [segments](https://kenpathlabs.com/lokah-samples/ai-voice-agent-calls--16.segments.json)
- Sample 17, Customer service: two-wheeler insurance claim (Gujarati), from a 4-minute conversation: [audio](https://kenpathlabs.com/lokah-samples/ai-voice-agent-calls--17.m4a) · [segments](https://kenpathlabs.com/lokah-samples/ai-voice-agent-calls--17.segments.json)
- Sample 18, Customer service: clinic appointment booking (Punjabi), from a 2-minute conversation: [audio](https://kenpathlabs.com/lokah-samples/ai-voice-agent-calls--18.m4a) · [segments](https://kenpathlabs.com/lokah-samples/ai-voice-agent-calls--18.segments.json)
- Sample 19, Customer service: e-commerce return and refund (Odia), from a 3-minute conversation: [audio](https://kenpathlabs.com/lokah-samples/ai-voice-agent-calls--19.m4a) · [segments](https://kenpathlabs.com/lokah-samples/ai-voice-agent-calls--19.segments.json)
- Sample 20, Customer service: mobile network complaint (Telugu), from a 4-minute conversation: [audio](https://kenpathlabs.com/lokah-samples/ai-voice-agent-calls--20.m4a) · [segments](https://kenpathlabs.com/lokah-samples/ai-voice-agent-calls--20.segments.json)
- Sample 21, Customer service: e-commerce return and refund (Hindi), from a 4-minute conversation: [audio](https://kenpathlabs.com/lokah-samples/ai-voice-agent-calls--21.m4a) · [segments](https://kenpathlabs.com/lokah-samples/ai-voice-agent-calls--21.segments.json)
- Sample 22, Customer service: e-commerce return and refund (Indian English), from a 3-minute conversation: [audio](https://kenpathlabs.com/lokah-samples/ai-voice-agent-calls--22.m4a) · [segments](https://kenpathlabs.com/lokah-samples/ai-voice-agent-calls--22.segments.json)

### Sample 1 transcript

Recruitment: phone screening (Marathi), starting at 0:00. Stereo: left channel is the caller, right channel is the ai voice agent. Audio sha256 `1aaaab343f6bffca02b87930b62bc331eab732b3191712f0c8492953ea93f4cf`.

| Start | Speaker | Text |
| --- | --- | --- |
| 0:01 | AI voice agent | नमस्कार! मी स्टाफिंग एजन्सीमधून बोलतेय. तुमच्या डेलिव्हरी पार्टनरच्या ॲप्लिकेशनबद्दल बोलण्यासाठी फोन केलाय. तुमचं नाव काय आहे आणि आता बोलायला योग्य वेळ आहे का? |
| 0:12 | Caller | अहो माझं नाव शिवानी आहे आणि सध्या हे बोलण्याची वेळ ठीक आहे. सांगा. |
| 0:19 | AI voice agent | धन्यवाद शिवानी. मला हे सांगा की तुम्ही सध्या कोणत्या शहरात आणि भागात राहता आणि तिथे किती दिवसांपासून आहात? |
| 0:27 | Caller | मी सध्या Bangalore मध्ये असते JP नगर मध्ये आणि नेमके दिड वर्ष झाले मी इथे आहे. |
| 0:35 | AI voice agent | अरे वा, दीड वर्ष म्हणजे चांगलीच माहिती झाली असेल जेपी नगरची! मग तुम्हाला तिथले कोणते लोकॅलिटीज चांगले माहिती आहेत? |
| 0:44 | Caller | JP मध्ये असे खूप काही आहेत जागा फिरायला वगैरे. |
| 0:49 | AI voice agent | छान आहे. अच्छा मला हे सांगा, तुमच्याकडे स्वतःची टू-व्हीलर आहे का? आणि तिची कंडिशन कशी आहे? आणि लायसन्स आहे का तुमच्याकडे? |
| 0:59 | Caller | सध्या तर माझ्याकडे काही two wheeler नाही आणि license पण काही नाहीये. |
| 1:05 | AI voice agent | अच्छा, म्हणजे तुमच्याकडे व्हीकल नाहीये. काही हरकत नाही. अच्छा, या आधी तुम्ही कोणतं काम केलंय का, त्याबद्दल थोडं डिटेल मध्ये सांगू शकाल? |
| 1:14 | Caller | कोणतं काम म्हणजे नेमकं कशाबद्दल विचारतो तुम्ही? |
| 1:19 | AI voice agent | माझा मतलब तुमच्या कामाच्या अनुभवाबद्दल होता. तुम्ही याआधी कुठे काम केलंय, किती वर्ष केलंय आणि ते काम का सोडलं, हे सांगू शकाल? |
| 1:28 | Caller | मी ह्या पहिले हैदराबादमध्ये काम केले होते आणि अशी खूप काही गोष्टी होते ज्यामुळे मी सोडले. |
| 1:35 | AI voice agent | अच्छा, हैदराबादमध्ये तुम्ही कोणत्या प्रकारचं काम केलं होतं? आणि ते काम किती काळ केलं आणि तिथे काही अडचण आली होती का काम सोडताना? |
| 1:44 | Caller | असं काही नाहीये. पण मला Bangalore बघायचं होतं म्हणून मी इकडे आले. |
| 1:49 | AI voice agent | अरे वा, बेंगलोर फिरण्यासाठी आलात, छान! अच्छा, तुम्हाला हा जॉब का हवाय, आणि तुम्ही याआधी कधी डिलिव्हरीचं किंवा कस्टमर-फेसिंग काम केलंय का? |
| 2:00 | Caller | मी आतापर्यंत कधी नाही केलं Delivery चं किंवा customer facing चं काम. हे पहिल्यांदा आहे माझं काम. |
| 2:09 | AI voice agent | काही हरकत नाही, हे ट्रेनिंगमध्ये सगळं शिकवलं जाईल. मी तुम्हाला आता या रोलबद्दल थोडी माहिती देते. यामध्ये वेगवेगळ्या शिफ्ट्स, डिलिव्हरीवर इन्सेंटिव्हज, फ्यूल अलाउन्स आणि वीकली पेमेंट्स मिळतील. याबद्दल तुम्हाला काय वाटतं? |
| 2:25 | Caller | Delivery incentives म्हणजे किती मिळेल? म्हणजे नेमके कशाबद्दल तुम्ही मला देता? |
| 2:32 | AI voice agent | इन्सेंटिव्हज हे तुम्ही किती जास्त डिलिव्हरी करता किंवा गर्दीच्या वेळी काम करता, त्यावर ठरतात. साधारणपणे, जास्त डिलिव्हरी केल्यावर अतिरिक्त उत्पन्न मिळू शकतं. तुम्ही कधी जॉईन होऊ शकता? |
| 2:44 | Caller | मी लगेच होऊ शकते असं. |
| 2:48 | AI voice agent | लगेच सुरू करू शकता? खूपच छान! तुम्हाला कोणती शिफ्ट करायला आवडेल, म्हणजे सकाळची, संध्याकाळची, आणि वीकेंड्सला काम करायला काही हरकत नाही ना? |
| 2:58 | Caller | असं काही नाही. मला नेमकं सकाळची करेल मी आणि weekends ला पण करायला काही problem नाही. |
| 3:05 | AI voice agent | ठीक आहे, सकाळची शिफ्ट आणि वीकेंडला काम करायला हरकत नाही. अच्छा, एक छोटी सिच्युएशन, जर कस्टमरने दरवाजा उघडला नाही तर तुम्ही काय कराल? |
| 3:15 | Caller | नेमकं मी त्यांना call करेल. |
| 3:18 | AI voice agent | बरोबर, त्यांना कॉल करून कन्फर्म करणं योग्य ठरेल. आता, पुढील प्रक्रियेबद्दल मी तुम्हाला सांगते. एक छोटीशी इन-पर्सन डॉक्युमेंट चेक होईल, ज्यासाठी तुम्हाला काही महत्त्वाची डॉक्युमेंट्स आणायची आहेत. |
| 3:31 | Caller | अच्छा ठीक आहे. कोणते आहेत मला सांगता का? |
| 3:35 | AI voice agent | तुम्हाला तुमचं आधार कार्ड, पॅन कार्ड आणि बँक डिटेल्ससाठी एक कॅन्सल्ड चेक किंवा पासबुकची कॉपी आणावी लागेल. हे सगळं तयार झाल्यावर, आपण पुढील स्टेपसाठी संपर्क साधू. तुम्हाला काही प्रश्न आहेत का? |
| 3:48 | Caller | हो मला नेमकं सांगता का की मला नेहमी किती रोज किती काम करावी लागेल, किती वेळ करावी लागेल आणि माझं location कुठलं येईल? |
| 4:00 | AI voice agent | साधारणपणे तुम्हाला रोज आठ तास काम करावं लागेल आणि तुमचं लोकेशन जेपी नगर आणि त्याच्या आसपासचा परिसर असेल. हे सांगा की तुम्हाला हा जॉब नेमका का हवाय? |
| 4:12 | Caller | कारण मला customers त्यांच्यासोबत भेटायला आणि त्यांच्यासोबत बोलायला connections वाढवायला आवडते म्हणून. |
| 4:21 | AI voice agent | हे खूप छान आहे शिवानी! कस्टमरशी जोडले जाण्याची तुमची इच्छा नक्कीच या जॉबमध्ये मदत करेल. माहिती दिल्याबद्दल धन्यवाद, आता मी हा कॉल संपवते. |
| 4:31 | Caller | हो चालते ना? |
| 4:34 | AI voice agent | ठीक आहे. सहकार्याबद्दल धन्यवाद. शुभ दिवस. |
| 4:38 | AI voice agent | काळजी घ्या, शुभ दिवस! |

## Licence

Licence: custom, quoted per use (training, evaluation or both; internal or commercial; exclusive or not). Delivered in the layout your training code reads: https://kenpathlabs.com/lokah/formats.

---

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