| dc.description.abstract | A telemarketing company relies heavily on its telemarketers to make numerous
calls to customers in order to promote the company products. To prioritize the
potential customers and evaluate the performance of telemarketers, a objectively
mechanism to identify which stage of promotion a call belongs to is crucial to a
telemarketing company. In this thesis, we design an audio call classification system
based on fine-tuned BERT [1] to automatically classify each telemarketer’s call to an
appropriate stage. The five components of the proposed system are data collection,
data pre-processing, pre-trained model fine-tuning, call-level classification, and the
web service. In data collection, the audio calls are converted into the corresponding transcripts via Kaldi speech recognition. In data pre-processing, transcripts
are processed to remove stopwords, split into segments, and assign labels manually. In pre-trained model fine-tuning, four BERT-based models are retrained to
obtain segment-level classification models. In call-level classification, a rule-based
method is performed to obtain the call-level classification (i.e., stage) of a call from
the classification results of the corresponding segments of the call. Finally, a web
service is provided to allow the company access the system easily. The extensive
experiments show that the proposed system reaches 97% Macro-F1 Score for the
call-level classification. | en_US |