Kaldi is an open source toolkit for speech recognition research. It provides a powerful framework for building state-of-the-art automatic speech recognition (ASR) systems, with support for deep neural networks, Gaussian mixture models, hidden Markov models, and other advanced techniques. The toolkit is widely used in both academia and industry due to its flexibility, extensibility, and strong community support. Kaldi is designed for researchers who need a highly customizable environment to experiment with new algorithms, as well as for practitioners who want robust, production-ready ASR pipelines. It includes extensive tools for data preparation, feature extraction, acoustic and language modeling, decoding, and evaluation. With its modular design, Kaldi allows users to adapt the system to a wide range of languages and domains. As one of the most influential projects in speech recognition, it has become a foundation for much of the modern work in ASR.
Features
- Comprehensive toolkit for building automatic speech recognition systems
- Supports neural networks, HMMs, GMMs, and hybrid ASR methods
- Provides tools for data preparation, feature extraction, and model training
- Highly flexible and extensible for research and custom experiments
- Actively used in academia and industry for speech research and deployment
- Large community with extensive examples, recipes, and documentation