COVAREP is an open-source repository of advanced speech processing algorithms and stored in a GitHub project where researchers in speech processing can store original implementations of published algorithms.
Over the past few decades a vast array of advanced speech processing algorithms have been developed, often offering significant improvements over the existing state-of-the-art. Such algorithms can have a reasonably high degree of complexity and, hence, can be difficult to accurately re-implement based on article descriptions. Another issue is the so-called 'bug magnet effect' with re-implementations requently having significant differences from the original ones. The consequence of all this has been that many promising developments have been under-exploited or discarded, with researchers tending to stick to conventional analysis methods.
By developing COVAREP we are hoping to address this by encouraging authors to include original implementations of their algorithms, thus resulting in a single de facto version for the speech community to refer to.
We envisage a range of benefits to the repository:
Reproducible research: COVAREP will allow fairer comparison of algorithms in published articles.
Encouraged usage: the free availability of these algorithms will encourage researchers from a wide range of speech-related disciplines to exploit them for their own applications.
Feedback: as a GitHub project users will be able to offer comments on algorithms, report bugs, suggest improvements etc.
We welcome contributions from a wide range of speech processing areas, including (but not limited to): Speech analysis, synthesis, conversion, transformation, enhancement, glottal source/voice quality analysis, etc.
Please also have a look at the Covarep.pdf document in the documentation directory for more information.
If you publish experiment results obtained by using COVAREP, please cite the repository using the following publication:
G. Degottex, J. Kane, T. Drugman, T. Raitio and S. Scherer, "COVAREP - A collaborative voice analysis repository for speech technologies", In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Florence, Italy 2014.Also, within the text of your paper, please mention the version used. For example, assuming the COVAREP reference is the 12th:
... we compared with methods X, Y, Z, available in (v1.0.1).