![]() Now is an exciting time to be working with cognitive services and voice data! Many different solutions are available for text to speech, speech to text, natural language understanding, and translation. So we need the following: a data store, a STT engine, a TTS engine, a NLU engine, and a translation service. Third, we need a text to speech (TTS) engine to speak to the user and tell them if the command succeeded or failed. Second, we need a natural language understanding (NLU) engine to detect intents, commands to act on. First, we need a speech to text (STT) engine to go from audio in to text in the native language. We also need services which let us accept voice commands. We also need a translation service to convert native speech to speech in a new language. To build out the above functions we need the following supporting services and components which let us translate speech.įirst, we need a data store to remember the user’s current spoken language. Tell the user what commands can be said: ‘what can I do here?’.Explicitly change to a language: ‘change my language to Spanish’.Swap language between English/Spanish: ‘switch my language’.Accept voice commands for the following:.Our translation bot needs to do the following tasks: Needed bot functionalityīefore we can build our bot, we need to decide what exactly we want it to do. One of our first offerings in this area is a translation bot which allows our users to translate between English and Spanish. Some of them work at big companies, some work at small ones, and many speak different languages.Īs we grow our business and develop our products, we are focusing on improving communication and providing voice integrations and solutions for customer workflows. At Orion Labs, we run a voice communications platform which allows teams to talk to each other using our wearable push-to-talk device, Onyx.Įvery day, our customers speak to each other over our network. In this post we are going to look at how and why we are building voice bots in the Python programming language. ![]()
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