Investigating Target Set Reduction for End-to-End Speech Recognition of Hindi-English Code-Switching Data
Table 4: Evaluation of attention and CTC based E2E systems developed using both reduced and unified target sets on Hindi-English code-switching data. The performances of reduced and unified target set based systems are measured using phone error rate (PER) and character error rate (CER), respectively. The performances for DNN-HMM system on those tasks are also given for reference purpose.
['[BOLD] Model', '[BOLD] Test1  [BOLD] PER', '[BOLD] Test1  [BOLD] CER', '[BOLD] Test2  [BOLD] PER', '[BOLD] Test2  [BOLD] CER', '[BOLD] Test3  [BOLD] PER', '[BOLD] Test3  [BOLD] CER', '[BOLD] Average  [BOLD] PER', '[BOLD] Average  [BOLD] CER']
For the unified target set case, the performances are measured in terms of the character error rate (CER). Whereas, for the reduced target set case, we have used the phone error rate (PER) as the measure. It can be observed that with proposed reduction in target set, all explored E2E systems yield significantly improved recognition performance (i.e., target error rate) over the corresponding unified target set based systems. Interestingly, this trend is carried over all the three test sets as defined earlier. On comparing the reduced target set systems, we note that the attention-based E2E ASR system has outperformed. Whereas, the CTC-based E2E system has yielded slightly better CER for the unified target set modelling case.