I am stuck in big problem, because I don't have any idea or example how to do it.
What i need to do is compare two voices, eg:
person 1 said "hello"
person 2 said "hello"
after that if person 1 say again "hello" system should be able to identify whether it's person 1 or person 2 (it is like voice authentication system).
I need to do this in C# or C++.
I found that "Microsoft speech API" and "synthetic toolkit" will help full for my task but I didn't found good tutorial or way to do it. Can anyone please help to success this task
If you're just looking for a library that will help you with this, check this page out.
http://en.wikipedia.org/wiki/List_of_speech_recognition_software
If your actual job is creating speech recognition code from scratch, then you're going to have a much more difficult job. Especially since there are several different routes you can go. Nearly all of them require some kind of feature extraction though (fft for example). So make sure you've prepped up on your signal processing methods. After you've extracted your features you can begin implementing several different methods. Hidden-markov models, neural networks (of many different varieties..), cross correlation...
Hidden markov model paper
http://mi.eng.cam.ac.uk/~mjfg/mjfg_NOW.pdf
Related
I don't know if this is the right place to ask this but, i am trying to build a bot in Python that will read incoming messages on a Slack channel where customer post their issues such as 'unable to connect to VPN', 'can someone reply to my ticket' etc…
The bot will analyze the message, determine if the customer is angry or not, and then propose a solution until an agent is free to actually check the issue.
Now, I was experimenting with TextBlob for the sentiment analysis part, but I don't know which technologies to actually use to determine the issue based on specific keywords and provide a solution to the user. Can someone propose me some python libraries/technologies that I could use to achieve this ?
To be honest your question is to generic to answer in one go.
Nontheless, you first have to clearly define the scope of your project. In doing so, you might want to first do a quick literaty review (Google Scholar) to familiarize with the state of the art technologies and methods.
From my little experience, a common (maybe simple) technique (lexicon-based approach) used to determine the sentiment of a word, is to use a pre-compiled dictionary (you can create your own though) that contains words - sentiment mappings. For example:
word:tired, sentiment:negative, score:5
So each time the bot finds the keyword "tired" in a sentence it will assign its corresponding negative value (polarity) to the sentence.
You might want to consider applying POS tags in the input text, as sometimes nouns or ``verbs carry significant meaning, compared to adjectives for example.
Keep in mind though, that negative comments can be written in the form of sarcasm. Sarcasm detectioin is a more difficult task though.
Alternatively, you could try using a pre-trained model such as bert-base-multilingual-uncased-sentiment that can be found here in Hugging Face.
For more information on the matter you have a look at this post.
Again as I mentioned, you have to clearly define your goals. This will enable you to specify the libraries or methodology available to solve your problem. Hope my answer helps.
My Question
What are the best practices for creating a customized report based on a user form input? Specifically, how do I create an easy to maintain system which takes user input which is collected in a form and generate multiple paragraphs that explains the results of analysis.
Background
I am working on a very large multiyear project with a startup (who is my client). My job is to program analysis and generate reports to users. The pipeline for data looks like this:
Users enter information into a form -> results are calculated based on user input -> reports are displayed to users that share analysis.
It is really important to my client that some of the analysis results are displayed in paragraphs in a non-formal user friendly tone. The challenge is that the form and analysis are quite complex and will only get more complex over time. An example of the type of template for the paragraphs looks something like this:
resultsParagraphText=`Hi ${userName}. We found that the best ice cream flavour for you is ${bestIceCreamFlavor}. These other flavors ${otherFlavors} might be good for you. Here are the reasons why you might enjoy these flavors: ${reasonsWhyGoodFlavors}.
However we would not recommend these other flavors ${badFlavors}. Here are the reasons you should avoid this bad flavors: ${reasonsWhyBadFlavors}.`
These results paragraphs, of which there of many, have several minor problems which combined are significant:
If there is a bug in the code, minor visual errors would be visible to end users (capitalization errors, missing/extra commas, and so on).
A lot of string comparisons (e.g. if answers.previousFlavors.includes("Vanilla")) are required to generate the results paragraphs. Minor errors in the forms (e.g. vanilla in the form is not capitalized so answers.previousFlavors.includes("Vanilla") returns false even when user enters vanilla.) can cause errors in the results paragraph.
Changes in different parts of the project (form, analysis) directly effect how the results paragraph is made. Bad types, differences in string values, null or undefined values not being caught directly have an impact on how the results paragraph is made.
There are many edge cases (e.g. What if the user has no other suitable good flavors for them? The the sentence These other flavors ${otherFlavors} might be good for you. needs to be excluded).
It is hard to write paragraphs that use templates and have a non-formal tone.
and so on.
I have charts and other types of ways to display results and have explained to the client the challenges of sharing the information in paragraph form.
What I am looking for
I need examples, how tos, best practices on how to build a maintainable system for generating customized paragraphs based on user input. I know how to solve each of the individual issues (as they are fairly simple) but in a large project this will become very hard to maintain.
Notes
I have no clue what tags to use for the post. Feel free to edit/add tags if you know more appropriate ones.
The project is planning to use machine learning in the future other parts of the project. If there is a ML/AI solution that is useful please tell me.
I am working primarily in JavaScript, Python, C, and R, but if there is a library or tool in any other language please tell me. Finding a solution is very important to me and I would be willing to learn a lot find a best solution.
To avoid this question being removed because I have rephrased it to avoid asking for personal opinion, instead asking for existing examples or how tos. I can also imagine that others might find a solution fairly useful. If you can edit it to make the question less subjective please do so.
If you have any questions or need clarification feel free to ask. Any help is appreciated.
I have 20,000 messages (combination of email and live chat) between my customer and my support staff. I also have a knowledge base for my product.
Often times, the questions customers ask are quite simple and my support staff simply point them to the right knowledge base article.
What I would like to do, in order to save my support staff time, is to show my staff a list of articles that may likely be relevant based on the initial user's support request. This way they can just copy and paste the link to the help article instead of loading up the knowledge base and searching for the article manually.
I'm wondering what solutions I should investigate.
My current line of thinking is to run analysis on existing data and use a text classification approach:
For each message, see if there is a response with a link to a how-to article
If Yes, extract key phrases (microsoft cognitive services)
TF-IDF?
Treat each how-to as a 'classification' that belongs to sets of key phrases
Use some supervised machine learning, support vector machines maybe to predict which 'classification, aka how-to article' belongs to key phrase determined from a new support ticket.
Feed new responses back into the set to make the system smarter.
Not sure if I'm over complicating things. Any advice on how this is done would be appreciated.
PS: naive approach of just dumping 'key phrases' into search query of our knowledge base yielded poor results since the content of the help article is often different than how a person phrases their question in an email or live chat.
A simple classifier along the lines of a "spam" classifier might work, except that each FAQ would be a feature as opposed to a single feature classifier of spam, not-spam.
Most spam-classifiers start-off with a dictionary of words/phrases. You already have a start on this with your naive approach. However, unlike your approach a spam classifier does much more than a text search. Essentially, in a spam classifier, each word in the customer's email is given a weight and the sum of weights indicates if the message is spam or not-spam. Now, extend this to as many features as FAQs. That is, features like: FAQ1 or not-FAQ1, FAQ2 or not-FAQ2, etc.
Since your support people can easily identify which of the FAQs an e-mail requires then using a supervised learning algorithm would be appropriate. To reduce the impact of any miss-classification errors, then consider the application presenting a support person with the customer's email followed by the computer generated response and all the support person would have to-do is approve the response or modify it. Modifying a response should result in a new entry in the training set.
Support Vector Machines are one method to implement machine learning. However, you are probably suggesting this solution way too early in the process of first identifying the problem and then getting a simple method to work, as well as possible, before using more sophisticated methods. After all, if a multi-feature spam classifier works why invest more time and money in something else that also works?
Finally, depending on your system this is something I would like to work-on.
This is my first time dabbling in NLP so please excuse my ignorance. I'm looking for a method to extract interests/likes/hobbies from users' social profiles. Here is an example where all the interests/likes/hobbies are in bold:
"I consider myself a pretty diverse character... I'm a professional
wrestler, but I'd take a bullet for Wall•E. I train like a one-man genocide machine in the gym, but I cried at
"Armageddon." I'll head bang to AC/DC, and I'm seriously
considering getting a Legend of Zelda tattoo. I'm 420-friendly. I
like to party it up with the frat crowd one night, hang out with
my Burning Man friends the next, play Halo and World of
Warcraft the next, and jam with friends that aren't any younger than
40 the next. My youngest friend is 16, my oldest friend is 66. I'll
sing karaoke at the bars, and I'm my friends' collective
psychiatrist/shoulder."
The profiles are plain text. There are no meta tags or ids associated with any of it, it's just a paragraph of text.
My naiive idea was to take each noun and match it against Freebase to see if it's an activity/artist/movie/book etc. The problem is that although most entities mentioned will be things the user likes, she will also mention things she doesn't like and I have no means of distinguishing the 2.
I have 2 questions:
What sub field of NLP should I be looking at? Some googleable algorithms/techniques/authors would be greatly appreciated.
How hard is this problem?
Thanks!
First, unless using NLP to do this is a particular objective for you, check your problem domain to see if you can avoid it completely.
For instance:
do these profiles have tags (supplied either by the Site or by the
user)?
what does the Site's API make available (assuming that's how you are accessing this data; if you are scraping it, then this doesn't of course apply)? A good example, Facebook. if you read a user's posts, you'll see words like "wrestler", "karaoke", etc. but if you look at what fields are exposed via the Graph API, you'll see that these activities nearly always have an associated FB ID.
I am not a specialist in this field, but I can recommend a couple of resources directed to NLP and which are accessible to the non-specialist or novice. The first is a text processing API. This simple web service uses REST and JSON IO. It is free and seems to have a fairly large rate limit.
This API appears to rely heavily on the excellent Natural Language Tooolkit (NLTK) which is a mature stable library in python, that includes modules directed to the problem in your Question, e.g., Sentiment Analysis, Tagging and Chunk Extraction, etc.
Which particular sub-domain is most relevant to solving the Question in the OP? I don't know, but I suspect there's a module somewhere in the NLTK that does what you need. Finding that module is hopefully just a matter of skimming the API Documentation (which is organized by module); reading the Getting Started section which contains an excellent survey of NLTK's modules as well as demos for all of each of them.
Don't know where to start on this one so hopefully you guys can clear up my question. I have project where email will be searched for specific words/patterns and stored in a structured manner. Something that is done with Trip it.
The article states that they developed a DataMapper
The DataMapper is responsible for taking inbound email messages
addressed to plans [at] tripit.com and transforming them from the
semi-structured format you see in your mail reader into a highly
structured XML document.
There is a comment that also states
If you're looking to build this yourself, reading a little bit about
Wrappers and Wrapper Induction might be helpful
I Googled and read about wrapper induction but it was just too broad of a definition and didn't help me understand how one would go about solving such problem.
Is there some open source project out there that does similar things?
There are a couple of different ways and things you can do to accomplish this.
The first part, which involves getting access to the email content I'll not answer here. Basically, I'll assume that you have access to the text of emails, and if you don't there are some libraries that allow you to connect java to an email box like camel (http://camel.apache.org/mail.html).
So now you've got the email so then what?
A handy thing that could help is that lingpipe (http://alias-i.com/lingpipe/) has an entity recognizer that you can populate with your own terms. Specifically, look at some of their extraction tutorials and their dictionary extractor (http://alias-i.com/lingpipe/demos/tutorial/ne/read-me.html) So inside of the lingpipe dictionary extractor (http://alias-i.com/lingpipe/docs/api/com/aliasi/dict/ExactDictionaryChunker.html) you'd simply import the terms you're interested in and use that to associate labels with an email.
You might also find the following question helpful: Dictionary-Based Named Entity Recognition with zero edit distance: LingPipe, Lucene or what?
Really a very broad question, but I can try to give you some general ideas, which might be enough to get started. Basically, it sounds like you're talking about an elaborate parsing problem - scanning through the text and looking to apply meaning to specific chunks. Depending on what exactly you're looking for, you might get some good mileage out of a few regular expressions to start - things like phone numbers, email addresses, and dates have fairly standard structures that should be matchable. Other data points might benefit from some indicator words - the phrase "departing from" might indicate that what follows is an address. The natural language processing community also has a large tool set available for text processing - check out things like parts of speech taggers and semantic analyzers if they're appropriate to what you're trying to do.
Armed with those techniques, you can follow a basic iterative development process: For each data point in your expected output structure, define some simple rules for how to capture it. Then, run the application over a batch of test data and see which samples didn't capture that datum. Look at the samples and revise your rules to catch those samples. Repeat until the extractor reaches an acceptable level of accuracy.
Depending on the specifics of your problem, there may be machine learning techniques that can automate much of that process for you.