I want to use sphinx4 for general purpose voice recognition, e.g. you tell the application something and it prints what you've said. However when I walk through the examples it's all about recognising a very small amount of vocabulary. Is there any good tutorial to help configure it to recognise something more challenging e.g. a dialog between two people?
PS: I believe that sphinx4 already has the correct acoustic models and dictionaries, but the lm file is for specific applications, so I'd need a lm file, correct?
However when I walk through the examples it's all about recognising with very small amount of vocabulary. Is there any good tutorial to help config it to recognise something more challenging, e.g. a dialog between two people.
You do not need to configure sphinx4. You can just checkout the latest version from subversion and use the demo as is, for more information see the tutorial
http://cmusphinx.sourceforge.net/wiki/tutorialsphinx4
When I believe is sphinx4 already included the right acoustic models and dictionaries, but the lm file is for the specific applications, so I'm needing a lm file, am I correct?
Default lm file provided is good enough for generic speech, however, if you have specific domain it makes sense to create your domain-specific language model.
Related
Good day. I am currently working on Machine Translation (Speech-(Text--Text)-Speech) with our local dialects and I already have the speech and text corpus. However, I am facing a problem in recording the speech as input and transcribing it to a text file because the modules available for speech recognition did not cover our dialects, mostly it just supports English and other major languages.
Is there anyone who know how I can fix it? I would be honored to accept your valuable suggestions and it will help me a lot on my studies. Thanks!
To work on text-to-speech for unusual dialects is a big challenge since frequently the audio models do not exist and have to be created from scratch. A good place to start is with one of the tutorials from http://voxforge.org. At this site you will find not only the tutorials involving a number of audio decoders and model generators but also a useful forum where students of various languages other than English have found solutions related to their own dialect problems.
A general plan might be as follows: build a simple English model following the examples given to get used to the terminology and concepts and process involved. Given your inevitable success with English, you can then turn your possession of a native corpus to advantage by building models for your own dialect. It is a reasonable goal and has been done many times before. Be warned however that to get good recognition across a broad vocabulary you will need a very comprehensive corpus data set.
My project is to extract events automatically from a given text. The events can be written specifically, or just mentioned in a sentence, or not be there at all.
What is the library or technique that I should use for this? I tried the Stanford NER demo, but it gave bad results. I have enough time to explore and learn a library, so complexity is not a problem. Accuracy is a priority.
The task itself is a complex one, it is still not solve now(2018). But recently a very useful python library for nlp is emerging. It is Spacy, this lib has a relative higher performance than its competitors. With the library you can do things like tokenize，POS tagging，NER and sentence similarity。But you still need to utilize these features and extract events based on your specific rule.
Deeplearning may also be a way to try, but even though it works fine on other nlp tasks, it is still immature on event extraction.
What approaches are there to generating question from a sentence? Let's say I have a sentence "Jim's dog was very hairy and smelled like wet newspaper" - which toolkit is capable of generating a question like "What did Jim's dog smelled like?" or "How hairy was Jim's dog?"
Thanks!
Unfortunately there isn't one, exactly. There is some code written as part of Michael Heilman's PhD dissertation at CMU; perhaps you'll find it and its corresponding papers interesting?
If it helps, the topic you want information on is called "question generation". This is pretty much the opposite of what Watson does, even though "here is an answer, generate the corresponding question" is exactly how Jeopardy is played. But actually, Watson is a "question answering" system.
In addition to the link to Michael Heilman's PhD provided by dmn, I recommend checking out the following papers:
Automatic Question Generation and Answer Judging: A Q&A Game for Language Learning (Yushi Xu, Anna Goldie, Stephanie Seneff)
Automatic Question Generationg from Sentences (Husam Ali, Yllias Chali, Sadid A. Hasan)
As of 2022, Haystack provides a comprehensive suite of tools to accomplish the purpose of Question generation and answering using the latest and greatest Transformer models and Transfer learning.
From their website,
Haystack is an open-source framework for building search systems that work intelligently over large document collections. Recent advances in NLP have enabled the application of question answering, retrieval and summarization to real world settings and Haystack is designed to be the bridge between research and industry.
NLP for Search: Pick components that perform retrieval, question answering, reranking and much more
Latest models: Utilize all transformer based models (BERT, RoBERTa, MiniLM, DPR) and smoothly switch when new ones get published
Flexible databases: Load data into and query from a range of databases such as Elasticsearch, Milvus, FAISS, SQL and more
Scalability: Scale your system to handle millions of documents and deploy them via REST API
Domain adaptation: All tooling you need to annotate examples, collect user-feedback, evaluate components and finetune models.
Based on my personal experience, I am 95% successful in generating Questions and Answers in my Internship for training purposes. I have a sample web user interface to demonstrate and the code too. My Web App and Code.
Huge shoutout to the developers on the Slack channel for helping noobs in AI like me! Implementing and deploying a NLP model has never been easier if not for Haystack. I believe this is the only tool out there where one can easily develop and deploy.
Disclaimer: I do not work for deepset.ai or Haystack, am just a fan of haystack.
As of 2019, Question generation from text has become possible. There are several research papers for this task.
The current state-of-the-art question generation model uses language modeling with different pretraining objectives. Research paper, code implementation and pre-trained model are available to download on the Paperwithcode website link.
This model can be used to fine-tune on your own dataset (instructions for finetuning are given here).
I would suggest checking out this link for more solutions. I hope it helps.
I have started working on a project which requires Natural Language Processing. We have do the spell checking as well as mapping sentences to phrases and their synonyms. I first thought of using GATE but i am confused on what to use? I found an interesting post here which got me even more confused.
http://lordpimpington.com/codespeaks/drupal-5.1/?q=node/5
Please help me decide on what suits my purpose the best. I am working a web application which will us this NLP tool as a service.
You didn't really give much info, but try this: http://www.nltk.org/
I don't think NLTK does spell checking (I could be wrong on this), but it can do parts of speech tagging for text input.
For finding/matching synonyms you could use something like WordNet http://wordnet.princeton.edu/
If you're doing something really domain specific: I would recommend coming up with your own ontology for domain specific terms.
If you are using Python you can develop a spell checker with Python Enchant.
NLTK is good for developing Sentiment Analysis system too. I have some prototypes of the same too
Jaggu
If you are using deep learning based models, and if you have sufficient data, you can implement task specific models for any purpose. With the development of deep leaning based languages models, you can used word embedding based models with lexicon resources to obtain synonyms and antonyms. You can also follow the links below to obtain more resources.
https://stanfordnlp.github.io/CoreNLP/
https://www.nltk.org/
https://wordnet.princeton.edu/
I'm looking to extract names and places from very short bursts of text example
"cardinals vs jays in toronto"
" Daniel Nestor and Nenad Zimonjic play Jonas Bjorkman w/ Kevin Ullyett, paris time to be announced"
"jenson button - pole position, brawn-mercedes - monaco".
This data is currently in a MySQL database, and I (pretty much) have a separate record for each athlete, though names are sometimes spelled wrong, etc.
I would like to extract the athletes and locations.
I usually work in PHP, but haven't been able to find a library for entity extraction (and I may want to get deeper into some NLP and ML in the future).
From what I've found, LingPipe and NLTK seem to be the most recommended, but I can't figure out if either will really suit my purpose, or if something else would be better.
I haven't programmed in either Java or Python, so before I start learning new languages, I'm hoping to get some advice on what route I should follow, or other recommendations.
What you're describing is named entity recognition. So I'd recommend checking out the other questions regarding this topic if you haven't already seen them. This looks like the most useful answer to me.
I can't really comment about whether NLTK or LingPipe is best suited for this task although from looking at the answers it looks like there's quite a few other resources written in Java.
One advantage of going with NLTK is that Python is very accessible as a language. The other advantage is that the NLTK book (which is available for free) offers an introduction to both Python and NLTK at the same time, which would be useful for you.