Companies like and Google (USM) are moving toward this unified architecture. When this matures, latency will drop by 50%, and accuracy will rise because the model can learn to ignore irrelevant acoustic variations (like a cough) that break pure-text models.
"timestamp": "2025-04-01T14:32:00Z", "confidence_score": 0.94, "intent": "order_food", "entities": "food_item": "pizza", "size": "large", "topping": "pepperoni", "delivery_address": "123 Main Street"
Call centers generate thousands of hours of audio. By converting calls to JSON, companies can automate Quality Assurance (QA). They can flag calls with negative sentiment or identify agents who are not following scripts.
Some JSON outputs contain no text at all. Instead, they contain "voice prints"—mathematical representations of a person's vocal tract. "user_id": "user_001", "voice_match_score": 0.987
Initiated by the EIT
Companies like and Google (USM) are moving toward this unified architecture. When this matures, latency will drop by 50%, and accuracy will rise because the model can learn to ignore irrelevant acoustic variations (like a cough) that break pure-text models.
"timestamp": "2025-04-01T14:32:00Z", "confidence_score": 0.94, "intent": "order_food", "entities": "food_item": "pizza", "size": "large", "topping": "pepperoni", "delivery_address": "123 Main Street" audio to json
Call centers generate thousands of hours of audio. By converting calls to JSON, companies can automate Quality Assurance (QA). They can flag calls with negative sentiment or identify agents who are not following scripts. Companies like and Google (USM) are moving toward
Some JSON outputs contain no text at all. Instead, they contain "voice prints"—mathematical representations of a person's vocal tract. "user_id": "user_001", "voice_match_score": 0.987 latency will drop by 50%