This is the modified version of Kaldi toolkit for Bayesian adaptation approaches proposed in the published paper: Xurong Xie, Xunying Liu, Tan Lee, & Lan Wang (2021). Bayesian Learning for Deep Neural Network Adaptation. IEEE/ACM Transactions on Audio, Speech, and Language Processing. (An updated version is available in arXiv https://arxiv.org/abs/2012.07460) This is originally proposed in the paper: Xurong Xie, Xunying Liu, Tan Lee, Shoukang Hu, & Lan Wang (2019, May). BLHUC: Bayesian learning of hidden unit contributions for deep neural network speaker adaptation. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 5711-5715).

The codes and scripts of the LHUC based adaptation techniques on end-to-end LF-MMI models are released (more clear scripts willed be released later), which are proposed in the paper: Xurong Xie, Xunying Liu, Hui Chen, Hongan Wang (2023). "Unsupervised model-based speaker adaptation of end-to-end lattice-free MMI model for speech recognition." Accepted by ICASSP 2023.

The codes are mainly based on src/nnet3, please find the running examples in egs/swbd/s5c.

                                                                       --By Xurong Xie

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The original readme

Kaldi Speech Recognition Toolkit

To build the toolkit: see ./INSTALL. These instructions are valid for UNIX systems including various flavors of Linux; Darwin; and Cygwin (has not been tested on more "exotic" varieties of UNIX). For Windows installation instructions (excluding Cygwin), see windows/INSTALL.

To run the example system builds, see egs/README.txt

If you encounter problems (and you probably will), please do not hesitate to contact the developers (see below). In addition to specific questions, please let us know if there are specific aspects of the project that you feel could be improved, that you find confusing, etc., and which missing features you most wish it had.

Kaldi information channels

For HOT news about Kaldi see the project site.

Documentation of Kaldi:

• Info about the project, description of techniques, tutorial for C++ coding.
• Doxygen reference of the C++ code.

Kaldi forums and mailing lists:

We have two different lists

• User list kaldi-help
• Developer list kaldi-developers:

To sign up to any of those mailing lists, go to http://kaldi-asr.org/forums.html:

Development pattern for contributors

1. Create a personal fork of the main Kaldi repository in GitHub.
2. Make your changes in a named branch different from master, e.g. you create a branch my-awesome-feature.
3. Generate a pull request through the Web interface of GitHub.
4. As a general rule, please follow Google C++ Style Guide. There are a few exceptions in Kaldi. You can use the Google's cpplint.py to verify that your code is free of basic mistakes.

Platform specific notes

PowerPC 64bits little-endian (ppc64le)

• Kaldi is expected to work out of the box in RHEL >= 7 and Ubuntu >= 16.04 with OpenBLAS, ATLAS, or CUDA.
• CUDA drivers for ppc64le can be found at https://developer.nvidia.com/cuda-downloads.
• An IBM Redbook is available as a guide to install and configure CUDA.

Android

• Kaldi supports cross compiling for Android using Android NDK, clang++ and OpenBLAS.
• See this blog post for details.

Web Assembly

• Kaldi supports cross compiling for Web Assembly for in-browser execution using emscripten and CLAPACK.
• See this post for a step-by-step description of the build process.