Speech analytics turns recorded conversations into structured, searchable, scoreable data. It is how modern teams understand what is actually being said on thousands of calls — without listening to each one.
Speech analytics is the technology that converts spoken audio into text, then analyses that text for meaning, emotion, keywords, compliance markers, and quality signals — automatically and at scale.
The audio is converted to text. This is the transcription layer — the accuracy of everything downstream depends on the quality of ASR. For Indian calls, this means the ASR must handle Hindi, Hinglish, Tamil, and other Indic languages accurately.
The text is analysed for meaning. NLP identifies entities (product names, prices, dates), emotions (frustration, satisfaction), topics, and intent. This is where keyword detection, sentiment analysis, and compliance checking happen.
The structured data from NLP is aggregated into scores, reports, and flags. Individual calls get quality scores; agent trends emerge; compliance violations surface. This layer turns data into decisions.
Call recording captures audio. Speech analytics makes it useful. A recording sitting in a folder is evidence — you can retrieve it if something goes wrong. Speech analytics is intelligence — it tells you what is going wrong before complaints are filed.
Most contact centres have call recordings. Very few have speech analytics applied to them. The gap between having recordings and having insights is exactly where teams lose.
Auto-detecting missing RBI/IRDA disclosures on loan and insurance sales calls before a Banking Ombudsman complaint is filed.
Identifying which agents consistently miss buying signals or struggle with price objections — then coaching on the exact transcript moment.
Flagging calls where customer frustration spikes — in retention teams, these are the calls that need a supervisor callback within 24 hours.
Tracking when customers mention competitor names across hundreds of calls — what triggers the comparison and how agents respond.
Most speech analytics platforms were built on English-language training data. When applied to Indian call centres — where agents and customers move freely between Hindi, English, Tamil, Telugu, and regional languages — accuracy collapses. A system that misses 30% of words cannot reliably detect compliance violations or sentiment.
Bolo Aur Likho's speech analytics is built on OpenAI Whisper, which has been trained on multilingual data including Indian languages, and processes Hinglish natively rather than forcing translation. The result is significantly higher accuracy on the actual language of Indian sales calls.
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