I've been working on a developing a product selection network for my workplace. I work with lots of chemicals and the clients don't always know what they want/need so most of the time I have to ask a bunch of question, collect useful/ignore useless information, then make a selection from there. Discussions take place over the phone.
Ideal situation In terms of information collection/flow: 1. I ask a probing question which specifies which feature I am referring to 2. Client answering question and speech recogniser converts voice to text filling in feature input. 3. Text summariser searches the feature and reduces down to specific key words. - For example: Me: "what kind of application are you looking to perform?" Client: "I'm wanting to adhere two pieces of wood together" Feature summarised to: adhere, wood 4. Once feature vector has enough information the network recommends the most suitable product.
Problems: 1. Clients tend to waffle and give useless information therefore network will need lots of training data. 2. Once a question is asked - client may not directly answer that question and may incidentally answer another feature question.
I would think the logical place to start would be a speech recognition RNN - I have written a weak tensorflow one however I think I'll just look to tap into Google's cloud speech recognition API here. This is where I get stuck - should I just use a simple forward/back propagation network from here and treat it as a classification problem or is there another way to do it?
Any direction pointers would be greatly appreciated.
Kind Regards, Andy