I have the x-y cordinates of some online handwriting samples from which I am computing some statistical parameters using which I want to make an HMM based recognizer.
As HMM toolkit or HTK is orginally made for Speech recognition, so I am not able to understand how to perform online word recognition using HTK.
Can someone please help me to understand how to give my parameters as input to this toolkit and what output it will give ?????
As you say, HTK was developed for speech recognition. The HTK toolkit is a collection of special purpose programs that all work together.
Here is a version of the manual that describes what each program was designed for, including expected inputs and outputs.
I will warn you though, you will have an uphill battle trying to use HTK for handwriting recognition. It simply wasn't written with that in mind. The opening paragraph of the linked manual says:
HMMs can be used to model any time series and the core of HTK is similarly general-purpose. However, HTK is primarily designed for building HMM-based speech processing tools, in particular recognisers. Thus, much of the infrastructure support in HTK is dedicated to this task.
Related
I see that there are advanced ML projects already that does text to speech such as SV2TTS : https://github.com/CorentinJ/Real-Time-Voice-Cloning
However what I am looking is rather than text to speech, re-synthesising speech into another voice
So are there any projects, software, library related to this subject that I can utilize?
I have found paid to have services but I need a free one for my experiments
Watson studio is a good software that includes libraries for speech recognition and speech translation. It might be able to help you with what you need to accomplish. I know that R has a built in library for this but it is very limited.
I am looking for a new MDA tool to try out for modelling and code generation. This is not for any work related project yet, but for testing purposes. I only used the Merode approach until now (using jMermaid for modelling and the accompagnied code generator) but want to try out something new.
Since EMF is integrated in Eclipse I see a lot of positive reasons to try it out. But after reading some documentation and online articles, I wonder how much it adopts the OMG MDA standards and how much it doesn't.
For example I found the following text
If, on the other hand, you have already bought into the idea of modeling, and even the Model Driven Architecture (MDA) big picture,3 you should think of EMF as a technology that is moving in that direction, but more slowly than immediate widespread adoption. You can think of EMF as MDA on training wheels.
on http://www.informit.com/articles/article.aspx?p=1323360&seqNum=2
But I can nowhere find a concise list of what points of the OMG standard are implemented and which ones are left out or interpreted differently. Anyone that can help out with that?
(And if there are other, more recommended tools, I'm always open to suggestions.)
There is very little relation. EMF is a framework to create (meta)models with very basic code-generation capabilities (basically only a Java direct translation). EMF's goal is not to be an MDA framework but to be the building block on top of which other tools may build more sophisticated solutions (e.g. check the open soruce Eclipse Acceleo tool).
And MDA is just a philosophy. Itself is not even a specific method. The MDA guide, the OMG standard document explaining MDA, is just a set of principles for model-driven development using OMG technologies but does not go further than that (if needed you may want to check the difference between all these MD* acronyms).
So, you can find EMF-based tools that follow MDA principles but EMF as such does not pretend to do so.
In EMF FAQ there is question "What is the relationship of EMF to OMG MDA?" which states
"Essentially EMF supports the key MDA concept of using models as input
to development and integration tools which produce multiple
programming language (Java in the case of Eclipse EMF itself) or data
interchange format (XML) representations."
EMF corresponds to a simplified OMG's MOF implementation (http://www.omg.org/mof/), providing facilities to express custom metamodels and generate java components to instantiate models.
MDA is a particular model-driven philosophy, based on several kind of models (CIM, PIM, PSM...), and aiming to provide a way to target several technical architectures (PSM) from a unique functional model (PIM).
You can use EMF for any model-driven philosophy MBE, MDE, MDD, or MDA. It is the fundamental building block that allows you to define your own metamodels and models. Simply said, EMF provides models, and you can use it for any model-driven approach, including MDA.
I'm interested in learning more about pattern recognition. I know that's somewhat of a broad field, so I'll list some specific types of problems I would like to learn to deal with:
Finding patterns in a seemingly random set of bytes.
Recognizing known shapes (such as circles and squares) in images.
Noticing movement patterns given a stream of positions (Vector3)
This is a new area of experimentation for me personally, and to be honest, I simply don't know where to start :-) I'm obviously not looking for the answers to be provided to me on a silver platter, but some search terms and/or online resources where I can start to acquaint myself with the concepts of the above problem domains would be awesome.
Thanks!
ps: For extra credit, if said resources provide code examples/discussion in C# would be grand :-) but doesn't need to be
Hidden Markov Models are a great place to look, as well as Artificial Neural Networks.
Edit: You could take a look at NeuronDotNet, it's open source and you could poke around the code.
Edit 2: You can also take a look at ITK, it's also open source and implements a lot of these types of algorithms.
Edit 3: Here's a pretty good intro to neural nets. It covers a lot of the basics and includes source code (albeit in C++). He implemented an unsupervised learning algorithm, I think you may be looking for a supervised backpropagation algorithm to train your network.
Edit 4: Another good intro, avoids really heavy math, but provides references to a lot of that detail at the bottom, if you want to dig into it. Includes pseudo-code, good diagrams, and a lengthy description of backpropagation.
This is kind of like saying "I'd like to learn more about electronics.. anyone tell me where to start?" Pattern Recognition is a whole field - there are hundreds, if not thousands of books out there, and any university has at least several (probably 10 or more) courses at the grad level on this. There are numerous journals dedicated to this as well, that have been publishing for decades ... conferences ..
You might start with the wikipedia.
http://en.wikipedia.org/wiki/Pattern_recognition
This is kind of an old question, but it's relevant so I figured I'd post it here :-) Stanford began offering an online Machine Learning class here - http://www.ml-class.org
OpenCV has some functions for pattern recognition in images.
You might want to look at this :http://opencv.willowgarage.com/documentation/pattern_recognition.html. (broken link: closest thing in the new doc is http://opencv.willowgarage.com/documentation/cpp/ml__machine_learning.html, although it is no longer what I'd call helpful documentation for a beginner - see other answers)
However, I also recommend starting with Matlab because openCV is not intuitive to use.
Lot of useful links on this page on computer vision related pattern recognition. Some of the links seem to be broken now but you may find it useful.
I am not an expert on this, but reading about Hidden Markov Models is a good way to start.
Beware false patterns! For any decently large data set you will find subsets that appear to have pattern, even if it is a data set of coin flips. No good process for pattern recognition should be without statistical techniques to assess confidence that the detected patterns are real. When possible, run your algorithms on random data to see what patterns they detect. These experiments will give you a baseline for the strength of a pattern that can be found in random (a.k.a "null") data. This kind of technique can help you assess the "false discovery rate" for your findings.
learning pattern-recoginition is easier in matlab..
there are several examples and there are functions to use.
it is good for the understanding concepts and experiments...
I would recommend starting with some MATLAB toolbox. MATLAB is an especially convenient place to start playing around with stuff like this due to its interactive console. A nice toolbox I personally used and really liked is PRTools (http://prtools.org); they have an implementation of pretty much every pattern recognition tool and also some other machine learning tools (Neural Networks, etc.). But the nice thing about MATLAB is that there are many other toolboxes as well you can try out (there is even a proprietary toolbox from Mathworks)
Whenever you feel comfortable enough with the different tools (and found out which classifier is perfomring best for you problem), you can start thinking about implementing the machine learning in a different application.
I would like to easily implement a data classification project, so I'm looking for the language which provides the library for that. Could you suggest the proper language?
matlab is not exactly a programming language, but no doubt it's the easiest way to implementing math oriented programs. it has lots of toolboxes for classifications (e.g. MLP, SVM) optimization toolboxes.
There is a Python distribution called SciPy that has lots of tools for scientific programming and people have used it to do data classification. Some bioinformatics people have built Excel2SVM in Python.
If the focus of your work is on the data classification, not on developing software, then Python is a good choice because you can be more productive than with languages like java or C++.
I'd say you really need more information before choosing a language.
Where are you getting data from, what front end do you want to use (web / dedicated client) ?
C# could do just as good a job, or any Object oriented language.
Cheers
(A little late coming, but I thought this answer should be here for the record).
WEKA and MALLET are two useful libraries for data classification that I've come across. I've used WEKA in a couple of projects and can say that it is pretty mature. Both these libraries are Java-based.
Could you recommend a training path to start and become very good in Information Extraction. I started reading about it to do one of my hobby project and soon realized that I would have to be good at math (Algebra, Stats, Prob). I have read some of the introductory books on different math topics (and its so much fun). Looking for some guidance. Please help.
Update: Just to answer one of the comment. I am more interested in Text Information Extraction.
Just to answer one of the comment. I am more interested in Text Information Extraction.
Depending on the nature of your project, Natural language processing, and Computational linguistics can both come in handy -they provide tools to measure, and extract features from the textual information, and apply training, scoring, or classification.
Good introductory books include OReilly's Programming Collective Intelligence (chapters on "searching, and ranking", Document filtering, and maybe decision trees).
Suggested projects utilizing this knowledge: POS (part-of-speech) tagging, and named entity recognition (ability to recognize names, places, and dates from the plain text). You can use Wikipedia as a training corpus since most of the target information is already extracted in infoboxes -this might provide you with some limited amount of measurement feedback.
The other big hammer in IE is search, a field not to be underestimated. Again, OReilly's book provides some introduction in basic ranking; once you have a large corpus of indexed text, you can do some really IE tasks with it. Check out Peter Norvig: Theorizing from data as a starting point, and a very good motivator -maybe you could reimplement some of their results as a learning exercise.
As a fore-warning, I think I'm obligated to tell you, that information extraction is hard. The first 80% of any given task is usually trivial; however, the difficulty of each additional percentage for IE tasks are usually growing exponentially -in development, and research time. It's also quite underdocumented -most of the high-quality info is currently in obscure white papers (Google Scholar is your friend) -do check them out once you've got your hand burned a couple of times. But most importantly, do not let these obstacles throw you off -there are certainly big opportunities to make progress in this area.
I would recommend the excellent book Introduction to Information Retrieval by Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze. It covers a broad area of issues which form a great and up-to-date (2008) basis for Information Extraction and is available online in full text (under the given link).
I would suggest you take a look at the Natural Language Toolkit (nltk) and the NLTK Book. Both are available for free and are great learning tools.
You don't need to be good at math to do IE just understand how the algorithm works, experiment on the cases for which you need an optimal result performance, and the scale with which you need to achieve target accuracy level and work with that. You are basically working with algorithms and programming and aspects of CS/AI/Machine learning theory not writing a PhD paper on building a new machine-learning algorithm where you have to convince someone by way of mathematical principles why the algorithm works so I totally disagree with that notion. There is a difference between practical and theory - as we all know mathematicians are stuck more on theory then the practicability of algorithms to produce workable business solutions. You would, however, need to do some background reading both books in NLP as well as journal papers to find out what people found from their results. IE is a very context-specific domain so you would need to define first in what context you are trying to extract information - How would you define this information? What is your structured model? Supposing you are extracting from semi and unstructured data sets. You would then also want to weigh out whether you want to approach your IE from a standard human approach which involves things like regular expressions and pattern matching or would you want to do it using statistical machine learning approaches like Markov Chains. You can even look at hybrid approaches.
A standard process model you can follow to do your extraction is to adapt a data/text mining approach:
pre-processing - define and standardize your data to extraction from various or specific sources cleansing your data
segmentation/classification/clustering/association - your black box where most of your extraction work will be done
post-processing - cleansing your data back to where you want to store it or represent it as information
Also, you need to understand the difference between what is data and what is information. As you can reuse your discovered information as sources of data to build more information maps/trees/graphs. It is all very contextualized.
standard steps for: input->process->output
If you are using Java/C++ there are loads of frameworks and libraries available you can work with.
Perl would be an excellent language to do your NLP extraction work with if you want to do a lot of standard text extraction.
You may want to represent your data as XML or even as RDF graphs (Semantic Web) and for your defined contextual model you can build up relationship and association graphs that most likely will change as you make more and more extractions requests. Deploy it as a restful service as you want to treat it as a resource for documents. You can even link it to taxonomized data sets and faceted searching say using Solr.
Good sources to read are:
Handbook of Computational Linguistics and Natural Language Processing
Foundations of Statistical Natural Language Processing
Information Extraction Applications in Prospect
An Introduction to Language Processing with Perl and Prolog
Speech and Language Processing (Jurafsky)
Text Mining Application Programming
The Text Mining Handbook
Taming Text
Algorithms of Intelligent Web
Building Search Applications
IEEE Journal
Make sure you do a thorough evaluation before deploying such applications/algorithms into production as they can recursively increase your data storage requirements. You could use AWS/Hadoop for clustering, Mahout for large scale classification amongst others. Store your datasets in MongoDB or unstructured dumps into jackrabbit, etc. Try experimenting with prototypes first. There are various archives you can use to base your training on say Reuters corpus, tipster, TREC, etc. You can even check out alchemy API, GATE, UIMA, OpenNLP, etc.
Building extractions from standard text is easier than say a web document so representation at pre-processing step becomes even more crucial to define what exactly it is you are trying to extract from a standardized document representation.
Standard measures include precision, recall, f1 measure amongst others.
I disagree with the people who recommend reading Programming Collective Intelligence. If you want to do anything of even moderate complexity, you need to be good at applied math and PCI gives you a false sense of confidence. For example, when it talks of SVM, it just says that libSVM is a good way of implementing them.
Now, libSVM is definitely a good package but who cares about packages. What you need to know is why SVM gives the terrific results that it gives and how it is fundamentally different from Bayesian way of thinking ( and how Vapnik is a legend).
IMHO, there is no one solution to it. You should have a good grip on Linear Algebra and probability and Bayesian theory. Bayes, I should add, is as important for this as oxygen for human beings ( its a little exaggerated but you get what I mean, right ?). Also, get a good grip on Machine Learning. Just using other people's work is perfectly fine but the moment you want to know why something was done the way it was, you will have to know something about ML.
Check these two for that :
http://pindancing.blogspot.com/2010/01/learning-about-machine-learniing.html
http://measuringmeasures.com/blog/2010/1/15/learning-about-statistical-learning.html
http://measuringmeasures.com/blog/2010/3/12/learning-about-machine-learning-2nd-ed.html
Okay, now that's three of them :) / Cool
The Wikipedia Information Extraction article is a quick introduction.
At a more academic level, you might want to skim a paper like Integrating Probabilistic Extraction Models and Data Mining to Discover Relations and Patterns in Text.
Take a look here if you need enterprise grade NER service. Developing a NER system (and training sets) is a very time consuming and high skilled task.
This is a little off topic, but you might want to read Programming Collective Intelligence from O'Reilly. It deals indirectly with text information extraction, and it doesn't assume much of a math background.