Speech to Text

Provide voice to your applications with AI-powered Speech-to-Text

Speech to Text

Overview

The Speech-to-Text service quickly and accurately converts the spoken word into precise, structured text with real time transcription, across English and multiple Indian languages. Whether to power a multilingual app, as an automated transcription service for customer support, or even enable reporting for on-field staff without needing to physically type or record responses, the Speech-to-Text platform provides the accuracy and speed required for modern voice-first digital workflows.

Specifically designed for Indian accents, noisy background environments, and code-mixed speech, this solution allows businesses to create more natural, efficient, and inclusive on-device experiences at scale.

Pricing

To know more about the SKUs and pricing click below.

Core Features at a Glance 

Automatic Speech Recognition (ASR)
Captures and translates spoken language to text with high accuracy, across various Indian languages and English.
Real-Time Transcription
Provides low-latency speech-to-text output to facilitate live interactions and conversational AI.
Multilingual & Code-Mixed Speech
Recognizes mixed-language, such as English with Hindi, Tamil or Telugu, and respects context.
Noise-Resistant Models
Trained in realistic environments, including outdoor noise, echo and low quality microphones.
Transform Accent to Regional Accent
Trained on a range of Indian accents, dialects, improving inclusivity.
Timestamped Transcripts
Generating time-coded output, to aid indexing, playback alignment and analytics.

What You Get

Still have questions?

STT works in English and the major Indian languages, And the languages that will work are Hindi, Tamil, Telugu, Marathi, Bengali, Kannada, and Malayalam.
Yes, it is optimized for ambient noise and will perform quite well in a lot of background noise situations.
It was built to specifically handle the code-mixed usage in India, especially if English+Hindi with good accuracy and can really depend on context.
Yes, this solution’s speaker diarization capability identifies speakers in the system by labeling who said what, which is useful for meetings and support calls.

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