This blog post is about IBM Cloud Watson Speech to Text (STT) language model customization. Currently I took a look at the IBM Cloud Watson Assistant service used to build conversational assistants. A conversation leads potentially to speech input of users, which needs to be converted to text to be processed using AI for example the NLU.
IBM Watson Assistant is a SaaS offering from IBM to build conversational assistants. IBM Watson Assistant is using artificial intelligence which helps to understand users in context, to provide them easy and fast, consistent, and accurate answers across various applications, devices, or channels. IBM Watson Assistant is built on natural language understanding (NLU), natural language processing (NLP) and machine learning (ML). The first version was already very good, and IBM clients and partners were starting to take these advantages; for example Watson Assistant was used at the International Space Station. Here you can find some more details: CIMON brings AI to the International Space Station. Based on the feedback from clients, the IBM development and design team has created a brand new experience and added new functionalities to the service for example they expanded the integration possibilities with extensions. In this blog post I focus especially on custom extensions development and setup.