Processing Speech Recognition Results With Wit.AI

The biggest challenge for developers today is a natural user interface. People already use gesture and speech to interact with their PCs and devices; such natural ways to interact with technologies make it easier to learn how to operate them. Biggest companies like Microsoft and Intel are putting a lot of effort into research in natural interaction.

CMUSphinx is a critical component of the open source infrastructure for creating natural user interfaces. However, it is not the only one component required to build an application. One of the most frequently asked questions are - how do I analyze speech recognition output to turn it into actionable information. The answer is not simple, again, it is all about a complex NLP technology which you can apply to analyze user intent as well as a dataset to help you with analysis.

In simple cases you can just parse the number strings to turn them into values, you can apply regex pattern matching to extract the name of the object to act upon. In Sphinx4 there exist a technology which can parse grammar output to assign semantic values in user request. In general, this is more complex task.

Recently, a Wit.AI has announced the availability of their NLP technology for developers. If you are looking for a simple technology to create a natural language interface, Wit.AI seems to be a good thing to try. Today, with the combination of the best engines like CMUSphinx and Wit, you can finally bring the power of voice to your app.

You can build a NLP analysis engine with Wit.AI in three simple stages:

  1. Provide a few examples of the responses you expect.
  2. Send raw user input to the API. You get structured information in return.
  3. Wit learns from usage and helps you improve your configuration.

Bringing natural language understanding to the masses of developers is really a hard problem and we great that tools appear to simplify the solution.