语音识别工具 Kaldi
- 授权协议: Apache
- 开发语言: C/C++
- 操作系统: Linux
- 软件首页: http://kaldi-asr.org/
- 软件文档: http://kaldi-asr.org/doc
- 官方下载: https://github.com/kaldi-asr/kaldi
软件介绍
Kaldi 是一个语音识别工具。使用 C++ 开发，基于 Apache 许可证。目的是为语音识别研究者提供。
Kaldi's versus other toolkits
Kaldi is similar in aims and scope to HTK. The goal is to have modern and flexible code, written in C++, that is easy to modify and extend. Important features include:
Code-level integration with Finite State Transducers (FSTs)
We compile against the OpenFst toolkit (using it as a library).
Extensive linear algebra support
We include a matrix library that wraps standard BLAS and LAPACK routines.
Extensible design
As far as possible, we provide our algorithms in the most generic form possible. For instance, our decoders are templated on an object that provides a score indexed by a (frame, fst-input-symbol) tuple. This means the decoder could work from any suitable source of scores, such as a neural net.
Open license
The code is licensed under Apache 2.0, which is one of the least restrictive licenses available.
Complete recipes
Our goal is to make available complete recipes for building speech recognition systems, that work from widely available databases such as those provided by the Linguistic Data Consortium (LDC).
The goal of releasing complete recipes is an important aspect of Kaldi. Since the code is publicly available under a license that permits modifications and re-release, we would like to encourage people to release their code, along with their script directories, in a similar format to Kaldi's own example script.
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.
