Future of texts

It seems that people will loose the ability to read, comprehend and remember long texts soon, the question now is - is it possible to deliver very complex messages without texts?
The critical issue is to design a flow of information into human brain which will both allow to scan though extremely large amounts of data and deduct new meanings. Text/speech is indeed quite slow channel for that, vision might be reasonable.

Visualization seems relevant if we want to keep human intelligence instead of replacing it with pure computer intelligence.  Works like LargeVis

Visualizing Large-scale and High-dimensional Data
by Jian Tang, Jingzhou Liu, Ming Zhang, Qiaozhu Mei

are much more important then. See also the LargeVis project on github.

The case against probabilistic models in metric spaces

A recent discussion on kaldi group about OOV words reminded me about this old problem.

One of the things that makes modern recognizers so unnatural is probabilistic models behind them. It's a core design decision to build the recognizer on terms of probability of classes and use models which are all probabilistic. Probabilistic models are easy to estimate, but they do not often fit the reality.

In the most common situation, if you have two classes A and B and garbage class G, a point from the garbage is either estimated as A or B and it is very hard to properly classify it as G. While probability of the signal is easy to estimate from the database based on examples, probability of the garbage is very hard. You need to have a huge database of garbage examples or you will not be able to get the garbage estimate properly. As a result, the current systems can not drop non-speech sounds and often create very misleading hypothesis. Bad things also happen in training, incorrectly labelled examples significantly disturb correct probability estimation and model has no means to detect them.

And in a long term the chase for probabilistic model is getting worse, everything is reduced to probabilistic framework. People talk about graphical models, Gaussian processes, stick-breaking model, Monte-Carlo sampling when they simply need to optimize the number of Gaussians in the mixture with a simple cost function. And they never tell you can simply train 500 Gaussians mixture and that will work equally well.

Same issue you might see in search engines, you can not use "not" in the search, for example, you can not search for a "restaurant not on the river bank". Though some companies try to implement such search, this effort is not widespread yet.

Situation slightly changes if we consider some real space of variants, for example a metric space. Much more reasonable decision might be made with geometrical models. You just look on the distance between the observation and the expectation and make a decision based on certain threshold. Of course you need to train the threshold and the distance function but this decision relies only on observation and the distance, not on the probability of everything else. Yes, I'm talking about plain old SVMs.

Metric is really the key here, with generic space indeed you can not invent something more advanced than simple bayesian rule. However, in presence of metric you might hope that you'll get much more interesting results from using it or at least combining metric decision with probabilistic decision.

Unfortunately there is no much information about it on the net, almost all AI books start with probabilistic reasoning as a natural approach to intelligence. I found some research like this paper, but it is far from being complete. Any links on more complete research  on the topic would be really appreciated.


IWSLT 2015

IWSLT 2015 proceedings recently appeared. This is an important competition in ASR focused on TED talks translation (and, more interesting for us, transcription).

Best system from MITLL-AFRL had a nice WER 6.6%.

It is interesting that most of the winner system (same was in MGB challenge Cambridge system ) were using combinations of customized HTK + Torch and Kaldi. Kaldi alone does not get the best performance (11.4%), plain custom HTK is usually better with WER 10.0% (see Table 8). And combination usually gives ground-breaking result.

There is something interesting here.


Harmonic Noise Model in Speech Recognition


Recently I came around a nice demo about generation of natural sounds from physical models. This is really an exciting topic because while Hollywood can now draw almost everything like Star Wars, the sound generation is pretty limited and unexplored area. For example, really high quality speech still can not be created by computers, no matter how powerful they are. This leads to a question of speech signal representation.

Accurate speech signal representations made a big difference in different areas of speech processing like TTS, voice conversion, voice coding. The core idea is very simple and straightforward but also powerful - we notice the fact that acoustic signals are either produced by harmonic oscillation in which case it has structure or by a turbulence cavitation in which case we see something like white noise. In speech such classes are represented by vowels and sibilant consonants, everything else is a mixture of those with some degree of turbulence and some degree of structure. However, this does not really speech-specific, all other real world signals except artificial ones might be analyzed from this point of view.

Such representation allowed to greatly improve voice compression in the class of MELP codecs (mixed excitations linear prediction). Basically we represent the speech as noise and harmonics and compress them separately. That allowed to improve compression of speech signal to unbelievable 600b/s. Mixed excitation was very important in text-to-speech synthesis. And it really made a big difference, as was proven quite some time ago by Mixed excitation for HMM-based speech synthesis by Takayoshi Yoshimura at al. 2001.

Unfortunately there is very little published research on mixed excitation models for speech recognition. I only found a paper A harmonic-model-based front end for robust speech recognition by Michael L. Seltzer which does consider harmonic and noise model but focus on robust speech recognition and not the advantages of the model itself. However, I believe such model can be quite important for speech analysis because it allows to classify speech events with very high degree of certainty. For example, if you consider a task of creating TTS system from voice recording, you might still notice that even best algorithms still confuse sounds a lot, assign incorrect boundaries, select wrong annotation. More accurate signal representation could help here.

It would be great if readers share more links on this, thank you!