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nfriedly

I'm responsible for the Watson Speech JS SDK, which is aimed at making the speech services easy to use in web apps.

Code is at https://github.com/watson-developer-cloud/speech-javascript-...

Simple demos at http://watson-speech.mybluemix.net/

More complex demo at https://speech-to-text-demo.mybluemix.net/

I'm goin to be out some this evening but feel free to ask me questions and I'll answer them as available.

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What's up with the lack of https and encryption in IBM's cloud? I'm sure your machine learning stuff is great and all but the lack of proper encryption and security measurements makes it a total no-go. How can you expect anybody to take your cloud services seriously when you do http in 2017? I'm asking as someone who had their boss talked into trying out ML stuff one Bluemix by your sales people.

Can you give me an example?

http://stream.watsonplatform.net/ (the domain that the speech APIs use) redirects to https. Ditto for http://gateway.watsonplatform.net/ which is what most of the other APIs use.

Both of the linked demos also redirect to https if you try a http URL.

Yes, most of your links (just like many of IBM's cloud related web services). If anything you, IBM and any potential customers, should be seriously concerned that they begin with http:// in the first place.

How does pricing[1] for this work? I see $0.02 / min of Speech to text. Is 1 minute a minimum transaction size or can you send over 6 transactions which in total add up to 1 min of recognition time?

1: https://www.ibm.com/watson/developercloud/speech-to-text.htm...

The final total is rounded up to the nearest minute at the end of each month and then the bill is based on that. So, in your 6 transaction example, you'd be billed for 1 minute as long as all 6 were within the same month.

Thanks! How much have you tested in environments with a lot of background noise? (in car, around kids or construction)

Is it also built for those situations, or mainly focused on accuracy in low ambient noise contexts?

I haven't done much high noise testing, but I am aware that it has better accuracy with low background noise.

I've been told that cell phones make a good mic in noisy environments FWIW.

How well would it work with technical or specific jargon? (Anatomy and medical terminology, in our specific use-case)

Is there a way to feed some kind of (text) dictionary to aid recognition? Or does it also need audio samples to learn from?

If you use Kaldi you can mix any type of domain-specific texts, it usually improves accuracy significantly, particularly for technical domains. You do not need audio for that.

Yes! You can upload a corpus of text that the service will learn new words (and their context) from and/or you can tell it it specific words and their pronunciation. No audio samples are needed, the customization works on the existing language models.

More details: https://www.ibm.com/watson/developercloud/doc/speech-to-text...

How accurate is the speaker diarization? How does it compare to LUIM?

Speaker Labels/diarization is still a beta feature, so it's a bit limited right now, but it's pretty accurate for two-person conversations of decent length. (The beta was primarily trained for phone conversations.)

I've never tried LUIM, so I can't comment there.

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