On the robustness of tiny decoding graphs for voice-based robotic interaction

In this paper we study the robustness of a command decoding approach based on tiny decoding graphs for voice-based robotic interaction. This approach comprises the fusion of the grammar rules and the statistical n-gram language models to produce an elegant and quite efficient tiny decoding graph. The resulting tiny graph has several advantages such as high speed and improved robustness of command decoding even in adverse noisy conditions. To validate the robustness of the proposed approach, we employed a set of spoken commands from the Resource Management (RM1) command and control corpus. These commands are artificially corrupted by 10 types of noise at different signal-to-noise ratios (SNRs). Experimental results show that the proposed approach achieved word error rates of 1.9% and 29% for the commands at 20dB and 5dB respectively, whereas the word error rates of the same task using the traditional grammar rules were 43% and 75% for the commands at 20dB and 5dB SNRs, respectively.