The phoneme recognizer was developed at Brno University of Technology, Faculty of Information Technology and was successfully applied to tasks including language identification , indexing and search of audio records, and keyword spotting . The main purpose of this distribution is research. Outputs from this phoneme recognizer can be used as a baseline for subsequent processing, as for example phonotactic language modeling.
The source code has been successfully compiled under Linux (GCC) and under Windows (MinGW32). The program can be compiled with or without BLAS (Basic Linear Algebra Subprograms) for acceleration. The ATLAS (Automatically Tuned Linear Algebra Software) is used in this case.
make -f makefile.lin
make -f makefile_noblas.lin
make -f makefile.win
make -f makefile_noblas.win
phnrec -c PHN_CZ_SPDAT_LCRC_N1500|PHN_HU_SPDAT_LCRC_N1500|PHN_RU_SPDAT_LCRC_N1500|
phnrec -c PHN_EN_TIMIT_LCRC_N500 -w alaw|lin16
phnrec -c PHN_EN_TIMIT_LCRC_N500 -l list -m out.mlf #!MLF!# "*/faem0.rec" 000000 1300000 pau 1300000 2000000 ah 2000000 3500000 s 3500000 4500000 ih
phnrec -c PHN_EN_TIMIT_LCRC_N500 -i input.raw -o output.rec
phnrec -c PHN_EN_TIMIT_LCRC_N500 -i input.raw -o output.rec -p -3.0
|System||# labels||ERR (%)|
Note: The Czech, Hungarian and Russian SpeechDat systems were used in NIST LRE2005.
Results obtained by this system can slightly differ from published ones due to implementation.
Source codes and binaries can be redistributed and/or modified under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. Model files (directories PHN_CZ_SPDAT_LCRC_N1500, PHN_HU_SPDAT_LCRC_N1500, PHN_RU_SPDAT_LCRC_N1500, PHN_EN_TIMIT_LCRC_N500) can be used for research and educational purposes only. For any other use, please contact Jan Cernocky.
This group was created for you. We are not able to personally answer/solve all questions, test the package for all possible platforms and explain in details the training scripts. So we decided to build a community around BUT phoneme recognizer that could share the knowledge. Please feel free to ask questions on this group email list. If you know already the answer to someone’s question, please help us to answer the question and guide others. Feel also free to upload any documents that you created and that could help others.
We developed two sets of training scripts. One based on the excelent QuickNet software from ICSI and a second one based on our STK toolkit. The one based on QuickNet can be used to train new set of neural networks for BUT phoneme recognizer directly. On the other hand, the STK based sripts work with the HTK feature files and use quite powerfull macro language which allows to easily set-up any TRAP based or Split Temporal Context based feature extraction. This can simplify the experiments.
[posteriors] softening_func=gmm_bypass 0 0 0
|||P. Schwarz, "Phoneme Recognition based on Long Temporal Context, PhD Thesis", Brno University of Technology, 2009|
|||P. Schwarz, P. Matejka, J. Cernocky, "Hierarchical Structures of Neural Networks for Phoneme Recognition", submitted for publication to ICASSP2006|
|||P. Schwarz, P. Matejka, J. Cernocky, "Towards Lower Error Rates in Phoneme Recognition", in Proc. TSD2004, Brno, Czech Republic, 2004|
|||P. Matejka, P. Schwarz, J. Cernocky, P. Chytil, "Phonotactic Language Identification using High Quality Phoneme Recognition", in Proc. Eurospeech2005, Sep, 2005|
|||I. Szoke, P. Schwarz, L. Burget, M. Fapso, M. Karafiat, J. Cernocky, P. Matejka, "Comparison of Keyword Spotting Approaches for Informal Continuous Speech", in Proc. Eurospeech2005, Sep, 2005|