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Looking for APIs, methods, research, etc on the subject of deciding whether a tweet (a string, really) conveys a mood of danger.
For example:
Danger: "this house across the street is on fire!!
Not danger: "this girl is on fire! love this song"
There is little research done on the particular problem of detecting danger, but there are a few research papers describing methods to detect natural hazards. Your example is reminiscent of the title of one of them: Finding Fires with Twitter. Another research that you may find useful is Emergency Situation Awareness: Twitter Case Studies.
In general, however, the best approach to solve such a problem is through supervised classification, very similar to how sentiment analysis is (or rather, was, because there are more sophisticated machine learning paradigms like Deep Learning being applied nowadays) done.
The essence is to label documents (in your case, tweets) into "danger" and "not danger". This labeling is done by human experts. Ideally, they should be well versed in the language and the domain. So, using native English speakers who know the colloquialisms of Twitter would be perfect annotators for this task.
Once adequate number of documents have been labeled, the baseline (i.e. the basic approach) is usually achieved by creating n-gram word vectors as feature vectors, and running SVM. If you are not aware of machine learning details, please read up on them before doing this.
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I need to be able to count the number of syllables a speaker utters during a live recording. It should be noted that the speakers will not be using their native language and therefore most existing speech recognition solutions won't work. I've looked a little at CoreML, and I may be able to get access to speech corpora with the number of syllables coded, but I'm not sure how I would start training the model.
I'd also be happy with imperfect but generally consistent approaches that didn't use machine learning.
(This will be used in linguistics research in future)
In terms of naive solutions, I've found this on Language Log, but I'm not sure how it would be implemented in Swift (this sort of audio analysis is not my forte).
In terms of machine learning solutions, I found this on GitHub, but any attempts at porting the Tensorflow v.1 code to run in CoreML failed.
Any help you could offer would be greatly appreciated!
It depends on the technique used. If you want to get syllables from the audio that is definitively words, then another strategy would be to use a pre-trained speech-to-text model, like Whisper, to get the transcript. From the transcript, you could run a simple algorithm on the text that counts the syllables in the transcribed text.
This method will not account for utterances that are faint, however. It will also take some wrangling to get it real-time, but it is is state-of-the-art transcription.
Another option is to use the Apple speech-to-text feature provided by the SDK. This tutorial, here, will guide you to build a speech-to-text app with real-time transcription. Using the transcribed words, you can count the syllables from the transcript.
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Thanks for making it this far on my post!
I am studying engineering, yet have a passion for programming and wish to implement computer science knowledge into my own research.
My question is pertaining to any resources that this community has available and any advice you all are willing to give regarding getting started in this broad field.
I’m mainly confused about ‘neural networks’ in relation to Deep Learning as well as implementation of algorithms.
I have slight Python and R knowledge.
Note: one of the subfora of StackExchange is probably a better fit for this question.
In any case, for ML you can do just fine with basic Python/R. Most of the research and work done on ML is based on TensorFlow and similar frameworks currently (2018). To use the frameworks you don't really need a strong programming background to setup and train models on them (although it certainly helps). Actually, math/statistics will help you more, specially if you want to get to the bottom of it (i.e. reading the latest articles/papers, etc.).
Mainly I’m confused about ‘neural networks’ in relation to Deep Learning
"Deep Learning" is basically taking advantage of modern computing capabilities to train complex models (e.g. neural networks with many hidden layers) which a few years ago (e.g. 10 years ago) were unfeasible. Informally speaking, the more complex your network is, the more interesting are the things that it can learn.
as well as implementation of algorithms.
Typically, you will use an existing framework -- you won't implement the algorithms yourself. Although, of course, implementing a MultiLayer Perceptron by yourself is always a good and fun learning exercise.
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Disclaimer: although I know some things about big data and am currently learning some other things about machine learning, the specific area that I wish to study is vague, or at least appears vague to me now. I'll do my best to describe it, but this question could still be categorised as too vague or not really a question. Hopefully, I'll be able to reword it more precisely once I get a reaction.
So,
I have some experience with Hadoop and the Hadoop stack (gained via using CDH), and I'm reading a book about Mahout, which is a collection of machine learning libraries. I also think I know enough statistics to be able to comprehend the math behind the machine learning algorithms, and I have some experience with R.
My ultimate goal is making a setup that would make trading predictions and deal with financial data in real time.
I wonder if there're any materials that I can further read to help me understand ways of managing that problem; books, video tutorials and exercises with example datasets are all welcome.
Take ML course on coursera. It is a good introductery into ML algorithms which will tell you what ML could do\some general approaches:
https://www.coursera.org/course/ml
Also to get a broader picture I suggest coursera's DataSciense course:
https://www.coursera.org/course/datasci
Finally a good book is Mahout in action - it is more about solving practical matters with mahout and has lots of examples and case-studies.
I beleive after that you will have a better understanding of what you want to do next.
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I just came up with an idea that I want to develop into an application to distinguish/auto detect voices from different people.
Sample use case: After training with Obama and Romney's data, the application would be able to detect whenever either one speak again (not necessary the same content from the training data)
I am wondering if there are any existing research on this. (I don't know how to search for this. I tried a couple keywords and got no significant results.)
If not, what is a good way to start? How to choose features, data representation, models, etc.
Thanks!
I found Speaker recognition on Wikipedia which in turn linked to An overview of text-independent speaker recognition: From features to supervectors (Kinnunen, Li, 2010).
From the abstract of the paper:
This paper gives an overview of automatic speaker recognition technology, with an emphasis on text-independent recognition. Speaker recognition has been studied actively for several decades. We give an overview of both the classical and the state-of-the-art methods.
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I have recently started studying Machine Learning and found that I need to refresh probability basics such as Conditional Probability, Bayes Theorem etc.
I am looking for online resources where I can quickly brush up probability concepts wrt Machine Learning.
The online resorces, I stumbled upon are either very basic or too advanced.
This might help: http://www.cs.cmu.edu/~tom/10601_fall2012/lectures.shtml
The above link is from Tom Mitchell's Machine Learning Class # CMU. Videos are available too. You will gain a very good understanding of ML concepts if you go through all the videos. (or just the first few videos for Conditional Probability, Bayes Theorem, etc).
The notion of conditional probability and bayes theorem are very basic themselves. It doesn't get any more basic than that in probabilistic modeling, you might say. Which suggests that you didn't look two well at what you've found or didn't really do any search at all.
Off the top of my head, I can name two resources: first, any Coursera course dealing with probabilities or machine learning (see AI, Statistics One or Probabilistic Graphical Models) contains these preliminaries. Second, there's a number of books on statistics freely available online, one example being Information Theory, Inference, and Learning Algorithms.