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 TOOLKITS


HTK

HTK (Hidden Markov Model Toolkit) is software toolkit for handling HMMs. It is mainly intended for speech recognition, but has been used in many other pattern recognition applications that employ HMMs, including speech synthesis, character recognition and DNA sequencing.

Originally developed at the Machine Intelligence Laboratory (formerly known as the Speech Vision and Robotics Group) of the Cambridge University Engineering Department (CUED), HTK is now being widely used among researchers who are working on HMMs.

Kaldi

Kaldi is a toolkit for speech recognition written in C++ and licensed under the Apache License v2.0. Kaldi is intended for use by speech recognition researchers.We have tried to make Kaldi's documentation as complete as possible given time constraints, but in the short term we cannot hope to generate documentation that is as thorough as HTK's. In particular there is a lot of introductory material in the HTKBook, explaining statistical speech recognition for the uninitiated, that will probably never appear in Kaldi's documentation. Much of Kaldi's documentation is written in such a way that it will only be accessible to an expert. In the future we hope to make it somewhat more accessible, bearing in mind that our intended audience is speech recognition researchers or researchers-in-training. In general, Kaldi is not a speech recognition toolkit "for dummies." It will allow you to do many kinds of operations that don't make sense.

Torch

Torch is a scientific computing framework with wide support for machine learning algorithms. It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.

The goal of Torch is to have maximum flexibility and speed in building your scientific algorithms while making the process extremely simple. Torch comes with a large ecosystem of community-driven packages in machine learning, computer vision, signal processing, parallel processing, image, video, audio and networking among others, and builds on top of the Lua community.

At the heart of Torch are the popular neural network and optimization libraries which are simple to use, while having maximum flexibility in implementing complex neural network topologies. You can build arbitrary graphs of neural networks, and parallelize them over CPUs and GPUs in an efficient manner.


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