Our approach yields recognition rates close to the ones obtained on the same corpus [4, 38 40], excerpt for certain metaphoric gestures: start system, wind it up, and beat both (the worst score is reached for this last category, with 39.1% recognition rate).
Our unadorned implementation yielded recognition accuracies that are slightly below the best results in [20],[24], but especially the fact that our system yielded higher accuracies in the −5-dB SNR condition than their systems with exemplars with a length of 50 ms corroborates our belief that we are on a promising track towards a novel approach to noise-robust ASR.
Systems include those for fruit recognition for yield mapping as well as those for disease detection systems using ground- and aerial-based platforms.
It will be shown in Section 6.4.4 that (8) and (9) yield comparable recognition accuracy.
In [41], different approaches based upon global representations of gestures are tested, and yield mean recognition rates between 80.8 and 91.9%.
In addition, the traditional approaches only take a single snapshot per plate to recognize, which is insufficient to yield accurate recognition results for that plate.
Experimental results show that the DuoNN architecture, configuration, and the additional emotional information processing, yield higher recognition rates and faster learning and decision making.
However, combining MSE with either of MVN and MVA can yield better recognition rates with regard to Test Set C. For example, MSE plus MVA (80.58%) is better than MVA alone (79.12%) in averaged recognition accuracy.
Both algorithms are shown to be effective in voice-tag applications, as they yield speech recognition performances either comparable to or exceeding a manual reference in same-language experiments.
Similarly, the most reliably recognized happy facial expressions did not yield lower recognition accuracy at the lower emotion intensity.
