Gradient-free decoding parameter optimization on automatic speech recognition

Finding the optimal decoding parameters in speech recognition is often done manually in a rather tedious manner, although automatic gradient-free optimization techniques have been shown to perform quite well for this task. While there have been recent scientific contributions in this field, no thorough comparison of possible methods, in terms of convergence speed and performance, has been undertaken. In this paper, we conduct a series of experiments with three decoding paradigms and four different optimization techniques found in recent literature, both on unconstrained and time-constrained decoder optimization. We offer our findings on the German Difficult Speech Corpus and on the LinkedTV test sets.