Residual Convolutional CTC Networks for Automatic Speech Recognition
Table 6: The comparisons of choosing 1-best and N-best hypotheses of each subsystem for system combination. The performance of N-best hypotheses is no better than 1-best regardless which voting schemes are used.
['[EMPTY]', 'WER-WSJ(%)', 'WER-Chat(%)']
Choice of 1-best vs. N-best in system combination. Here N-best (N=10) distinct hypotheses of each subsystem are prepared for combination. Firstly, if we use the voting scheme in Section 4.4, i.e., maximal confidence score voting, choosing N-best does not offer any further benefits. Although N-best hypotheses make the WTN contain more branchings and words choices, maximal confidence score voting almost gets the same result as with 1-best hypothesis. Moreover, we conduct another experiment using frequency of occurrence as voting score for N-best subsystems combination. We find that the results are close to 1-best on WSJ data set but slightly worse on Tencet Chat data set. This is because that one subsystem’s error may repeat many times in N-best hypotheses, which distorts the following frequency-based voting. Furthermore, considering the computational cost of N-best hypotheses, 1-best from each subsystem with maximal confidence score may be preferred.