If a company invests in multiple markets, they should be prepared to do well in some markets and badly in others. Bing isn't as good as Google. Android isn't as well-designed as Metro. Yes, Android stole Apple's market, and, yes, Apple stole someone else's market. The large technology companies are deadlocked on multiple fronts. That fuels fierce competition and inspires excellence and choice. However, companies should accept they just aren't the best at everything. Let us make our own choices based on what's best for us.

But impressive and very useful.

"Speech is a continuous audio stream where rather stable states mix with dynamically changed states. In this sequence of states, one can define more or less similar classes of sounds, or phones.

Words are understood to be built of phones, but this is certainly not true. The acoustic properties of a waveform corresponding to a phone can vary greatly depending on many factors - phone context, speaker, style of speech and so on. The so called coarticulation makes phones sound very different from their “canonical” representation. Next, since transitions between words are more informative than stable regions, developers often talk about diphones - parts of phones between two consecutive phones. Sometimes developers talk about subphonetic units - different substates of a phone. Often three or more regions of a different nature can easily be found.

The number three is easily explained. The first part of the phone depends on its preceding phone, the middle part is stable, and the next part depends on the subsequent phone. That's why there are often three states in a phone selected for HMM recognition.

Sometimes phones are considered in context. There are triphones or even quinphones. But note that unlike phones and diphones, they are matched with the same range in waveform as just phones. They just differ by name. That's why we prefer to call this object senone. A senone's dependence on context could be more complex than just left and right context. It can be a rather complex function defined by a decision tree, or in some other way."

(Sorry, I know I'm being cranky)

The main difference here is hooking DNN output to an HMM decoder, replacing GMMs, and possibly even more important the training process they use to get the DNN fairly efficiently. That's the biggest thing -- GMMs, at least the last time I've looked, can be trained and adapted much quicker than a DNN.

I think the HTK doesn't use neural networks at all. What it does is simply computes the MFCC of the sound signal and use it as input to a chain of HMM models. Well, "simply" that, plus the dozens of refinements and tweakings to make that work well.

Here, I guess they do some sort of preprocessing on the sounds features using their deep neural networks before feeding the whole thing to the HMMs.

They basically are using a new (in the context of speech rec) technique that seems to improve accuracy by 16% relative on their test data (and using their code :-)). It's a really great result, but it doesn't change the basic nature of a state of the art speech recognizer at all -- you still need to train and adapt it -- and it still needs lots and lots of data.