From the source
The rigid structure of a keyboard struggles to capture the fluidity of Indian languages, and for most people, speaking is simply more natural than typing.
From farmers checking crop prices, to gig workers receiving navigation instructions, to elderly users navigating WhatsApp and smart TVs, voice is the default mode of interaction.
This reality presents both opportunity and challenge.
Traditional automatic speech recognition (ASR) systems perform well on clean, read-speech benchmarks, but they often break down in real-world Indian settings.
In practice, accuracy alone is an insufficient lens.
Speech recognition in India must go beyond transcription.
Three core challenges stand out: First, script control Indian speakers freely mix English into their speech.
In some applications, English words must be preserved in Roman script while in others they must be transliterated into the native script.
A single fixed output format does not work.
Second, multi-speaker separation Real-world audio often involves multiple speakers talking simultaneously.
Accurate recognition requires not just transcription, but reliable speaker identification and attribution.
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