nsh - Speech Recognition With CMU Sphinx
Blog about speech technologies - recognition, synthesis, identification. Mostly it's about scientific part of it, the core design of the engines, the new methods, machine learning and about about technical part like architecture of the recognizer and design decisions behind it.
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.
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.
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!
We did some experiments with NMF and other methods to robustly recognize overlapped speech before but my conclusion is that unless training and test conditions are carefully matched the whole system does not really work, anything unknown on the background really destroys recognition result. For that reason I was very interested to check recent progress in the field. The research is pretty early stage but there are very interesting results for sure.
The talk by Dr. Paris Smaragdis is quite useful to understand connection between non-negative matrix factorization and more recent approach with neural networks which also demonstrate how neural network works by selecting principal components from the data.
One interesting bit from the talk above is the announcement of the bitwise neural networks which are very fast and effective way to classify inputs. I believe it could be another big advancement in the performance of the speech recognition algorithms. The details could be found in the following publication: Bitwise Neural Networks by Minje Kim and Paris Smaragdis. Overall, the idea of the bit-compressed computation to reduce memory bandwidth seem very important (LOUDS language model in Google mobile recognizer also from this area). I think NVIDIA should be really concerned about it since GPU is certainly not the device this type of algorithms need. No more need in expensive Teslas.
Another interesting talk was by Dr.Tuomas Virtanen in which a very interesting database and the approach to use neural networks for separation of different event types is presented. The results are pretty entertaining.
This video also had quite important bits, one of them is the announcement of Detection and Classification of Acoustic Scenes and Events Challenge 2016 (DCASE 2016) in which acoustic scene classification would be evaluated. The goal of acoustic scene classification is to classify a test recording into one of predefined classes that characterizes the environment in which it was recorded — for example "park", "street", "office". The discussion of the challenge which starts soon is already going in the challenge group, this would be very interesting to participate.
by Thilo Stadelmann et al
This is not mainstream research, but it is exactly what makes it interesting. The main idea of the paper is that to understand and develop speech algorithms we need to advance our tools to assist our intuition. This idea is quite fundamental and definitely has interesting extensions.
Modern tools are limited, most developers only check spectrograms and never visualize distributions, lattices or the context dependency trees. N-grams are also rarely visualized. In speech the paper suggests to build tools not just to view our models, but also to listen for them. I think this is quite a productive idea.
In modern machine learning tools visualization definitely helps to extend our understanding of complex structures. Here a terrific Colah's blog comes to mind. It would be interesting to extend this beyond pictures.
Take a text, take an LM, computer perplexity:
Join every two lines in text:
This is a really serious issue for decoding of conversational speech, the perplexity raised from 158 to 183, in real-life cases it's getting even worse. WER drops accordingly. So many times utterances contain several sentences and it's really crazy that our models can't handle that properly.
This practice has been started long long time ago during NIST evaluations I think, when participants reported system combination WER. NIST even invented ROVER algorithm for better combination.
For me personally such content in a paper reduces quality of the paper significantly. The system combination WER was never meaningful addition. Yes, it's well known that if you combine MFCC with PLP you can reduce WER by 0.1% and probably you will be able to win the competition. From scientific point of view this result adds zero new information, it just a filler for the rest of your paper. Also, to get a combination result of 5 systems you usually spend 5 times more computing individual results. Not worth for 0.1% improvement, you can usually get the same with slightly wider beams.
So instead consider doing something else, try to cover the algorithms you used and explain why do they work, try to describe the troubles you've solved, try to add new questions you consider interesting. At least try to collect more references and write a good overview on the previous research. That will save your time, reader's time and the computing power you used to build another model.
- ► 2011 (14)
- ► 2010 (42)