Voice data across India and multilingual markets.
Sonexis supports real conversational voice data across Indian English, Hindi, Hinglish, Punjabi, Marwadi, and code-switched formats based on buyer requirements.
Five supported languages.
Indian English
EN-INUse cases
ASR evaluation, voice agents, support conversations, multilingual benchmarks.
Speech behaviour
Regional variation, Indian phrasing, mixed vocabulary, informal flow.
Hindi
HIUse cases
ASR training, customer support, onboarding, conversational AI.
Speech behaviour
Regional accent variation, formal and informal switching, short replies.
Hinglish
HI-ENUse cases
Voice agents, support, product discovery, real-world multilingual testing.
Speech behaviour
Natural Hindi-English switching, mixed sentence structure, informal phrasing.
Punjabi
PAUse cases
Regional speech testing, multilingual voice datasets, support scenarios.
Speech behaviour
Accent variation, mixed Hindi or English flow, informal speech.
Marwadi
MWRUse cases
Regional voice data, underrepresented language testing, local conversation flows.
Speech behaviour
Regional vocabulary, Hindi-adjacent expressions, and longer contextual phrasing less common in standard training data.
Languages in combination.
Code switching is natural in Indian speech. These combinations reflect how people actually speak, not a forced separation of languages. Models and evaluation sets that treat languages in isolation can fail on within-utterance switches, which are common in real customer conversations.
Hindi-English
The most common code-switched combination in urban Indian speech.
Hindi-Marwadi
Regional switching across Rajasthan and adjacent areas.
Tamil-English
Common in urban Tamil Nadu and South Indian markets.
Hindi-Punjabi
Natural switching across North Indian and diaspora contexts.
Additional languages where scope is clear.
Available based on project scope and speaker requirements. Contact us to discuss what you need.
India is not a single-language market.
Real conversations often shift between languages within a single utterance, not just across turns. Users interrupt, correct themselves, reply briefly, speak in noisy environments, and mix language and register depending on context and speaker. A model or evaluation set built on clean single-language speech can miss these patterns entirely.
Language choice in a voice dataset is a design decision. It should reflect how the target user actually speaks, including accent variation, informal phrasing, regional vocabulary, and natural conversational behaviour, not what is easiest to collect.
Request language coverage.
Tell us the languages you need, the use case, and the volume. We will confirm whether coverage is available or can be built.
Scope a Dataset