praccu

Uh, at Amazon, junior devs go for around $120 if they don't negotiate well. Interns, last I checked, were close to $80.

If you fold in the TCO (benefits, computer / chair / licenses, etc.) for a senior engineer, the $350 isn't too far off.

This is a totally shameless plug, but here's some a PR thing about some work in that direction:

http://www.businesswire.com/news/home/20160216005731/en/Voca...

DNNs have lead to massive improvements in speech, specifically in the acoustic model. RNN based language models interpolated with modified kn smoothed ngrams (often plus some other cool stuff) are also the state of the art, although they're often too slow to use in practice.

cobaltspeech.Com -- in case you can't get what you want from a default Kaldi install.

/wave

Lemme know if you've got questions.

> 'There are a lot of problems in speech besides transcription. '

I'm trying to be vague because I don't want to violate NDAs and it's a pain to think up examples I'm not currently working on, but there are a lot of applications of machine learning to the _speech signal_ (rather than to the problem of speech to text; there are many other things you can predict) that are super interesting. Some examples are listed in the text I copied.

> 'a half-baked siri' There are so many instances where speech-to-text is super useful for applications other than consumer facing CRUD apps. In a lot of varied industrial applications people are doing very cool things with speech; there are a lot of applications that need very targeted speech systems.

There are a lot of problems in speech besides transcription. I should have been clearer about what I meant.

Even for people that do want transcription, though, often you can handily beat the accuracy of off-the-shelf services by tuning the models to the domain.

I'm pasting some copy from our website in part because I can't talk too much about specifics, but here's some examples:

"

High quality speech to text transcription, including in the very difficult areas of conversational human-to-human speech: phone calls, voicemails, meetings, etc. Classification of speech & language: identifying gender, age, regional accents, level of education, etc. Voice front-ends for various applications and devices, including natural language interfaces: heads-up displays, robots, smartphone apps, etc. Speech synthesis (a.k.a. TTS) to generate high-quality synthesized speech from text. Analysis of audio: detecting different types of noise or speech: dog bark, shouting, gunshot, water running, cars, etc. Customization of speech recognition to processed signals, such as proprietary compression or noise reduction algorithms Fine-grained analysis of speech and language, for giving feedback in language pathology or language learning. Analysis of speech & language to detect various health conditions: stroke, dementia, depression, schizophrenia, etc. Information extraction from text or audio: phone numbers, dates, entities and relationships, etc. " [0]

[0] http://cobaltspeech.com/our-products.html

Some state-of-the-art industrial speech recognition [0] is transitioning from HMM-DNN systems to "CTC" (connectionist temporal classification), i.e., basically LSTMs. Kaldi is working on "nnet3" which moves to CTC, as well.

Speech was one of the places where HMMs were _huge_, so that's kind of a big deal.

[0] http://googleresearch.blogspot.com/2015/09/google-voice-sear...

Yes. Language learning, for example. Speech recognition is being used for a lot of IoT stuff, home automation, and toys and the like. Lots of other really interesting niche applications.

We do research consulting in speech & language, and tons of small start-ups come to us with a variety of really interesting problems.

cobaltspeech.com

Huh?

The linked paper is 4 years old. DNNs have been dominant in speech since 2012. No one uses GMM systems anymore.

Baidu's approach isn't even the best (IBM's system tends to beat theirs on accuracy, and google tends not to publish numbers on known benchmarks), it's notable mostly for its use of RNNs to do pronunciation and language modeling (although they also tack on a mod-KN LM).

Kaldi is the best, in my opinion. It has a few open acoustic models. Unfortunately, speech is just hard; acoustic models are onlying one part of these systems.

aah. Okay. Disclaimer, [b] this isn't my area [/b] and I'm going to miss some folks. I apologize to all of the important and interesting work that I'm missing.

Also, I'm really linking to research groups, so take these names as starting points and look at their students and other professors working with them.

First off, the Blizzard challenge is a major hub of activity. [4] Festival is an important piece of software [5]. Interspeech is an important conference [6] (take a look at the speech synthesis track and the organizers for that).

Alan Black [0] @ CMU is kinda a giant.

Keiichi Tokuda [3] @ Nagoya also a giant.

Simon King [1] @ Edinburgh, just had a paper linked to on HN a few days ago and does important work.

Mark Gales [2] @ Cambridge does work here, too.

[0] https://www.cs.cmu.edu/~awb/ [1] http://www.cstr.ed.ac.uk/ssi/people/simonk.html [2] http://mi.eng.cam.ac.uk/~mjfg/ [3] http://www.sp.nitech.ac.jp/~tokuda/ [4] http://festvox.org/blizzard/ [5] http://www.cstr.ed.ac.uk/projects/festival/ [6] http://interspeech2015.org/wp-content/uploads/direct/INTERSP...

Did you try the chipmunk voice?

Ivona, acquired by Amazon, is a hugely notable example. They're great folks, and do great work. Alexa'a voice was done by them, but an early customer of theirs was the Polish public transit system.

A lot of really great work is happening in academia; I'm not going to name names because I'd forget someone deserving.

(Shameless plug: we [0] do speech and language consulting including custom TTS.)

[0] cobaltspeech.com

http://www.cobaltspeech.com ?

It's interesting that graphics and objects are so connected, in your posts. I'll say that in general, EVERYTHING in Python is objects, and they're an extremely general programming paradigm. It sounds like Haskell might be a language you'd like, if you're into abstract algebra (although I don't think Haskell's graphics support is super great).

Jumping (back) onto the Kendall food comment train:

Other places I haven't seen mentioned:

-- Squeaky Beaker

-- Boca Grande

-- Take-out from Similans

Also, what happened to the MIT food court and when is it coming back? I want terrible Chinese food again.

Momogoose for life. There are a bunch of food trucks behind the T stop.

Also, check out Aceituna.

If you're seriously interested in the anthropology, this is your guy:

http://en.wikipedia.org/wiki/Mark_Nathan_Cohen

EDIT: He's quoted in the article you linked, actually.