Speech-to-Text AI Built for Indian Telephonic Conversations

Most speech to text converter models are trained on the audio equivalent of a quiet room: audiobooks, podcasts, studio-grade recordings. A customer calling a contact center from a crowded street, through a low-quality headset, speaking fast and switching between two languages mid-sentence, is a different problem entirely. That mismatch is why generic transcription models degrade sharply on real customer calls, producing errors that cascade into failed intent detection, unnecessary reprompts, and frustrated callers.

Akshara, ConvoZen’s voice to text AI engine, is built against that exact problem: telephonic, conversational, code-switched Indian speech, not clean read-aloud audio.

How Is Speech-to-Text AI Built for Indian Conversations?

Akshara, as an AI speech-to-text system, has been assessed in nine Indian languages. This ai voice to text evaluation includes both the public benchmark (Indic Voices + Vaani) and ConvoZen’s own Indic Telephonic Voice Benchmark, which was developed specifically to include background noise, channel compression, and the speech patterns associated with fast conversational dialogue that are not encountered by studio-trained models.

How Accurate Is ConvoZen Speech-to-Text AI?

On the combined benchmark across all datasets, this speech AI records the lowest Word Error Rate (WER) of the three models evaluated:

This gives Akshara a relative reduction in WER of 32% compared to Sarvam Saaras v3 and a 55% relative reduction when compared to ElevenLabs Scribe v2; on the public Indic Voices + Vaani benchmark, it comes first in eight of the nine languages, being only outperformed by Sarvam in Telugu by a narrow margin of 0.5 points.

The gap widens on telephonic audio specifically, which is the condition that matters most for contact-centre deployments:

Akshara achieves the best results in all nine languages on this telephonic benchmark, showing the largest improvements in Kannada, Telugu and Tamil, languages in which the competitor’s WER rises to near or above 80%. The degree of improvement compared to the next best model varies from about 10% to 67% according to the language and benchmark in question, the median improvement across all the tested conditions being approximately 17%.

Speech-to-Text Accuracy in Short, Real-World Conversations

The accuracy achieved in long-form speech only gives us part of the picture. Voice AI interactions consist of short and quick exchanges such as “Yes,” “Kal subah,” “1234”, not full paragraphs. A model that does well in five-minute monologues may still have difficulty with these brief, high-frequency exchanges, since it is in such situations that a large proportion of containment failures in voice AI systems arise. While most public ASR benchmarks use longer and cleaner utterances, this voice to text converter is evaluated on Akshara’s telephonic benchmark, based on real contact-centre and enterprise call recordings.

How Does Speech-to-Text Accuracy Impact Voice AI?

The accuracy of speech-to-text AI is not just a single metric; it is the first stage in a process where errors carry forward through the subsequent stages. In ConvoZen’s voice agent architecture, transcription takes about 100 milliseconds as part of a pipeline with fixed latency.

A mistake that occurs during the STT phase doesn’t only result in a word being reported incorrectly; it instead passes on the wrong intention to the LLM, which then produces a response to a question that the customer had never actually asked. Reducing the WER at this stage is what helps to keep the number of reprompts low and the containment rates high throughout the rest of the conversation. Since the same system is also used to run ConvoZen’s AI voice generator for the response part, transcription accuracy and the quality of the generated speech are based on a single, integrated stack rather than being handled by two separate vendors.

How Can Businesses Deploy ConvoZen Speech-to-Text AI?

The speech-to-text converter offered by ConvoZen is designed so that engineering teams can integrate it quickly rather than having to go through an extensive onboarding process. The platform is used in contact-centre and enterprise voice processes by customers such as Cars24, Zell Education, Jana Small Finance Bank, Lendingkart, Pilgrim, and NoBroker, covering sectors including automotive, edtech, BFSI, and D2C retail.

The accuracy of Speech-to-Text AI on a clean benchmark dataset does not indicate how the system will perform during an actual customer call. That is why Akshara has been developed and assessed with a view to closing that gap, taking into account Indian languages, telephonic conditions, code-switching, and short conversational turns, and the benchmark data supports this: it shows the lowest WER across all 9 languages when using real telephonic audio, compared to both a leading Indic model and a global ASR provider.