![]() With the TwiML verb, Twilio provides a text-to-speech (TTS) function that uses Amazon Polly voices which leverage deep learning to synthesize human-like speech. ![]() Robot #2: Giving your robot a voice with Amazon Polly Neural Voices Historically, Twilio developers have used speech recognition as a way to navigate interactive voice response (IVRs) and other self-service automation workflows, but with the release of new experimental speech models, the only limit is ✨ your imagination ✨. It offers excellent accuracy, low latency, and support for numerous languages and dialects. Twilio's speech recognition using the TwiML verb is a powerful tool that turns words spoken on a phone call into text. Want to give this a demo a whirl before diving in? Call 1-989-4OPENAI (467-3624) to test it out! Robot #1: Decoding the human voice using Speech Recognition You'll also use the Call Event API to parse what callers are asking about and view the responses from the bot which allows us to unlock the rich first-party data captured in these interactions and send the data to customer engagement platforms like Segment where you can use it to build customer profiles, understand customer preferences, and create the personalized experiences customers expect. In this post, we'll show you how to use Twilio's native speech recognition and Amazon Polly Neural text-to-speech capabilities with ChatGPT to create a voice-activated chatbot, all hosted entirely on Twilio's serverless Functions environment. These days people talk to, listen to, and collaborate with robots all the time, but you know what's cooler than interacting with one robot? Interacting with three! Using ChatGPT to power an interactive voice chatbot is not just a novelty, it can be a way to get useful business intelligence information while reserving dedicated, expensive, and single-threaded human agents for conversations that only humans can help with.
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