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Microsoft Research make breakthrough in audio speech recognition

blogs.technet.com

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Re: Microsoft Research make breakthrough in audio speech recognition

#2
Can someone please explain senones to me? Can't find much on Google.

The article says that they are a fragment of a phoneme, but how small a fragment are we talking? 2-3 per phoneme, or many more?

Also - I'd be curious how much the phoneme in a word can vary based on accent.

Re: Microsoft Research make breakthrough in audio speech recognition

#3

Can someone please explain senones to me? Can't find much on Google. The article says that they are a fragment of a phoneme, but how small a fragment are we talking? 2-3 per phoneme, or many more? Also - I'd be curious how much the phoneme in a word can vary based on accent.

http://cmusphinx.sourceforge.net/wiki/tutorialconcepts

"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."

Re: Microsoft Research make breakthrough in audio speech recognition

#7
post #3

Can someone please explain senones to me? Can't find much on Google. The article says that they are a fragment of a phoneme, but how small a fragment are we talking? 2-3 per phoneme, or many more? Also - I'd be curious how much the phoneme in a word can vary based on accent.

http://cmusphinx.sourceforge.net/wiki/tutorialconcepts "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 m…

Thanks. So senones are not just fragments of phones - two senones could sound exactly the same, but be classified differently depending on their context within the audio stream.

Re: Microsoft Research make breakthrough in audio speech recognition

#8
Vlingo, Siri, and others have been doing speaker independent auto-adapting speech recognition for years and talking about systems requiring 'training' and improvements there sound like this article is 5 years old. Great to see innovation in this space but this article is very light on detail.

Re: Microsoft Research make breakthrough in audio speech recognition

#9
How does this compare to Microsoft's Old HTK (HMM Toolkit)? The language used on the website seems to point to a lot of the same things. Is this breaking it down to actual IPA phonemes?

I'm mostly curious because I used the HTK for my thesis and would like to know how they compare (besides, one being just 'newer').

Re: Microsoft Research make breakthrough in audio speech recognition

#10
post #8

Vlingo, Siri, and others have been doing speaker independent auto-adapting speech recognition for years and talking about systems requiring 'training' and improvements there sound like this article is 5 years old. Great to see innovation in this space but this article is very light on detail.

That jumped out at me as well. Speaker-independent systems are most certainly not limited to small vocabularies or pre-baked input patterns anymore. There is certainly room for a great deal of improvement, but it's in accuracy, not simply the ability to do generalized speaker-independent input at all.
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