In this work, we report benchmark results and enhancements made to PFP [ 13, 14] and ESG [ 16] as preparation for the CAFA2 experiment, prior to participation.
The services supplied by the NCBO and GoPubMed offered us a benchmark to evaluate the enhancement of performance of our method.
Laurie Harbour-Felax has more than 21 years of experience providing the automotive industry with benchmarking, manufacturing assessments, performance enhancement and strategic planning.
We used the training dataset provided by the "REVERB challenge" (reverberant voice enhancement and recognition benchmark) [40].
The recently released REverberant Voice Enhancement and Recognition Benchmark (REVERB) challenge includes a reverberant automatic speech recognition (ASR) task.
Hence, our proposed E2M rule is an enhancement of the benchmark EXP rule, which is commonly used in LTE packet scheduling.
The REverberant Voice Enhancement and Recognition Benchmark (REVERB) challenge is an Audio and Acoustic Signal Processing (AASP) challenge sponsored by the IEEE Signal Processing Society in 2013, and has recently been released for studying reverberant speech enhancement and recognition techniques [1].
This paper presents extended techniques aiming at the improvement of automatic speech recognition (ASR) in single-channel scenarios in the context of the REVERB (REverberant Voice Enhancement and Recognition Benchmark) challenge.
Here too, we see that there is a significant enhancement to the benchmark, and that the precision improvement curves are not equal for the different elapsed time parameters, meaning that different time scales bring different information.
In order to provide a common evaluation framework for developing and testing of algorithms in the fields of dereverberation as well as reverberation-robust ASR, the REverberant Voice Enhancement and Recognition Benchmark (REVERB) challenge [8] has been launched and REVERB contributions showed significant improvements for speech enhancement (cf., e.g., [9]) and ASR (cf., e.g., [10, 11]).
