Marathi conversations rarely stay in one language. Agents mix Marathi with English mid-sentence, customers quote UPI IDs and addresses in Hinglish, and calls run through noisy 8kHz telephone lines, not studio microphones. ConvoZen converts this real-world Marathi speech, spoken on calls, in meetings, and in recordings, into accurate, searchable, workflow-ready text that support, sales, QA (quality assurance, the process of reviewing calls to check agent performance), compliance, and analytics teams can act on directly.
Speech to text Marathi is the process of converting spoken Marathi audio into written text using an AI model trained specifically on Marathi speech patterns. ConvoZen’s Marathi speech to text runs on Akshara, ConvoZen’s proprietary speech-to-text (STT) model built for Indian languages.
Most voice typing tools are built for clean, single-speaker dictation recorded close to a microphone. Akshara is built for the opposite case: phone calls, overlapping speakers, regional accents, and code-switching, where a speaker moves between Marathi and English inside a single sentence. Marathi audio to text output from ConvoZen is used across support reviews, sales coaching, compliance audits, and customer analytics.
ConvoZen’s Marathi speech to text converter handles different sources of Marathi speech:
Whether the source is a live call or an archived recording, the output is the same: a structured Marathi transcript ready for review, documentation, or analysis.
Indian business conversations move between Marathi and English inside the same sentence, mixing in product names, numbers, IDs, and locations. ConvoZen’s Akshara STT is pre-trained on 50,000-plus hours of audio and fine-tuned on over 4,000 hours of hand-annotated data, which is how it follows these mid-sentence language switches without breaking the transcript.
Akshara is trained specifically on 8kHz telephony audio, the lower-quality format used on real phone calls, rather than high-fidelity studio recordings. It is built to handle the noisy, fast, overlapping speech common on contact-centre and mobile calls. In ConvoZen’s independent benchmark against two other leading Indic ASR (automatic speech recognition) models, Akshara recorded a Marathi Word Error Rate (WER, the percentage of words a model gets wrong) of 16.04% across combined evaluation conditions, 16.6% lower than the next-best model tested. On telephonic call audio specifically, the format closest to real contact-centre conditions, Akshara’s Marathi WER was 22.01%, against 27.69% and 50.45% for the two comparison models. Across all nine languages tested, Akshara’s overall accuracy is a 32% improvement over the next-best model and 55% over the third.
A Marathi transcript is only useful if a team can act on it. ConvoZen turns Marathi speech into structured output:
| Workflow | How Marathi Speech to Text Helps |
| Support Teams | Review queries, complaints, and resolutions faster across Marathi-speaking customers |
| Sales Teams | Track lead intent, objections, and follow-ups from Marathi sales calls |
| QA Teams | Check response quality and process adherence on every Marathi conversation, not a sample |
| Compliance Teams | Review disclosures and policy adherence in regulated Marathi conversations |
| Operations Teams | Spot trends, delays, and escalations across Marathi call volumes |
| Training Teams | Use real Marathi transcript examples for agent coaching |
| Content Teams | Create notes, captions, and searchable records from Marathi recordings |
Zell Education, an edtech platform, used ConvoZen’s automated call transcription and scoring across its counselling calls and saw a 7%-plus uplift in lead-to-conversion rate and a 60%-plus reduction in manual QA effort, with 100% visibility into every conversation
ConvoZen’s Marathi transcription is built to plug into the systems teams already use:
This means Marathi transcript data does not stay locked in a transcription tool. It moves into the dashboards, CRMs, and review systems teams already rely on.
Jana Small Finance Bank deployed ConvoZen’s voice AI across 9-plus Indian languages, including Marathi, for customer outreach. “We couldn’t get the latency and orchestration right in-house. With ConvoZen, it became seamless to test and run multiple use cases with a much more human-like experience,” said Giridhar Amerlai, Head of AI and Innovation at Jana Bank. The deployment contributed to a 10% boost in resolution rate and 7% sales growth from voice AI in sales workflows. Teams also using ConvoZen’s Ragini text-to-speech model can convert text back into natural-sounding audio, with sub-200ms audio generation latency useful for confirmations, IVR prompts, or notifications.
ConvoZen runs a dedicated stack of AI models for each customer, with data classification, localisation, and logical separation between accounts. The platform has undergone vulnerability assessment and penetration testing (VAPT) and holds ISO, GDPR, and HIPAA-aligned controls, with SOC2 certification in progress
Convert Marathi speech into accurate, searchable transcripts. Use them across support, sales, QA, and compliance workflows. Move from raw audio to review, analysis, and action.
It converts spoken Marathi audio, from calls, meetings, recordings, and voice notes, into written text for business use.
Yes. ConvoZen's Akshara model is trained on Marathi-English code-switched audio, so mid-sentence language mixing transcribes accurately.
Yes. Upload recorded calls, meetings, voice notes, or video audio for batch transcription into Marathi text.
Support, sales, QA, compliance, operations, training, and content teams use them for review, coaching, audits, and reporting.