Kaldi Toolkit

Key Features

• 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).