Performance Comparison between Lab-VIEW and MATLAB on Feature Matching-based Speech Recognition System

Speech recognition systems are widely used for smart applications. The smart application-based speech recognition system has different requirements for processing the human voice. The most common performance in the speech recognition system is essential to observe, since it is necessary to design smart application-based speech recognition systems for people's needs. Moreover, feature matching is the principal part of speech recognition systems since it plays a key role to authenticate, separate one human voice from another, and their different articulation. Therefore, this work proposes a performance comparison of speech recognition systems based on feature matching using Lab-VIEW and MATLAB. The feature extraction involves calculation of Mel Frequency Cepstral Coefficients (MFCC) for each frame. For the matching process, the system was tested 100 times for each five speeches by making changes in articulation with the same vocal cords. This matching process uses DTW (Dynamic Time Warping), and then the testing is based on the most common performance in the speech recognition system’s comparison between Lab-VIEW and MATLAB such as accuracy rate, execution time, and CPU usage. Based on experimental results, the average accuracy rate of MATLAB is better than Lab-VIEW. The execution time testing indicates that Lab-VIEW has a shorter execution time than MATLAB. On the other hand, Lab-VIEW and MATLAB have almost the same CPU usage. This result indicates that the performance comparison is able to be used according to the requirements of smart application-based speech recognition systems.

@article{Widasari2022,
  title     = {Performance Comparison between Lab-VIEW and MATLAB on Feature Matching-based Speech Recognition System},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {1},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Edita Rosana Widasari and Barlian Henryranu Prasetio and Dian Eka Ratnawati},
  doi       = {10.14569/IJACSA.2022.0130150},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130150}
}