ClovaCall: Korean Goal-Oriented Dialog Speech Corpus for Automatic Speech Recognition of Contact Centers
Table 4: CER of each ASR model on two datasets under three training schemes. A, QA, CC denote AIHub, QA Call, ClovaCall datasets, respectively. pt and ft mean the data for pretraining and finetunig. NA and SA are noise augmentation [ko2017study] and specaugment [park2019specaugment] on ClovaCall-Full.
['Models (Parameters)', 'DS2\xa0[amodei2016deep] (56M)', 'DS2\xa0[amodei2016deep] (56M)', 'LAS\xa0[chan2016listen] (31M)', 'LAS\xa0[chan2016listen] (31M)']
Our experiments focus on verifying the effectiveness of task-specific speech corpora for a certain AICC services. In the pretraining and finetuning scheme, despite the largest size of the general domain dataset, AIHub, the performance of ASR models trained from only AIHub is very poor. We conjecture this poor performance results from the differences between open domain and goal-oriented dialog datasets as shown in Fig. On the other hand, when pretrained with AIHub and finetuned with QA Call or ClovaCall, both models show remarkable improvement. This supports the necessity of using task-specific data for ASR models in real-world goal-oriented services.