Title :
The Bavieca open-source speech recognition toolkit
Author_Institution :
Boulder Language Technol. (BLT), Boulder, CO, USA
Abstract :
This article describes the design of Bavieca, an open-source speech recognition toolkit intended for speech research and system development. The toolkit supports lattice-based discriminative training, wide phonetic-context, efficient acoustic scoring, large n-gram language models, and the most common feature and model transformations. Bavieca is written entirely in C++ and presents a simple and modular design with an emphasis on scalability and reusability. Bavieca achieves competitive results in standard benchmarks. The toolkit is distributed under the highly unrestricted Apache 2.0 license, and is freely available on SourceForge.
Keywords :
C++ language; learning (artificial intelligence); public domain software; software reusability; speech processing; speech recognition; Bavieca open source speech recognition toolkit; C++ language; SourceForge; acoustic scoring; feature transformations; lattice-based discriminative training; model transformations; n-gram language models; phonetic context; reusability; scalability; speech research and system development; unrestricted Apache 2.0 license; Acoustics; Context; Decoding; Estimation; Hidden Markov models; Lattices; Training; automatic speech recognition;
Conference_Titel :
Spoken Language Technology Workshop (SLT), 2012 IEEE
Conference_Location :
Miami, FL
Print_ISBN :
978-1-4673-5125-6
Electronic_ISBN :
978-1-4673-5124-9
DOI :
10.1109/SLT.2012.6424249