An Audio Call Classification System Based on Fine-Tuned BERT

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Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/86546

Title:	An Audio Call Classification System Based on Fine-Tuned BERT
Authors:	賴議翔;Lai, Yi-Shiang
Contributors:	資訊工程學系
Keywords:	BERT;遷移學習;通話分類;BERT;Transfer learning;Call classification
Date:	2021-07-27
Issue Date:	2021-12-07 12:57:25 (UTC+8)
Publisher:	國立中央大學
Abstract:	一家電話行銷公司非常依賴他們的銷售員撥打大量的通話以推銷公司的產品，為了能夠優先處理較有購買意願的潛在客戶以及檢視銷售員的業績，一個能夠客觀判斷一通促銷通話目前屬於哪個促銷階段的機制對電話行銷公司非常重要。在這篇論文中，我們設計了一套基於微調 BERT 的語音通話分類系統，它能夠自動的將每通銷售員的通話分類為適當的階段。我們的提出的系統包含五個組件，包含資料收集、資料前處理、預訓練模型微調、通話等級分類、以及網路服務，在資料收集中，語音通話會藉由 Kaldi 語音辨識轉換為相對應的文本，在資料前處理，文本會經由移除停用詞、切割文本、以及手動標記等處理，在預訓練模型微調中，四個基於 BERT 的預訓練模型經由遷移學習進而獲得可對段落等級分類的模型，在通話等級分類中，一個基於規則的方法被用在通話相對應的段落上進而獲得一通通話的分類結果(階段)，最後我們提供了一個網路服務以便公司可以容易地使用我們的系統。經過密集的實驗後，結果顯示我們提出的系統在通話等級的分類上可以達到 97%的 Macro F1 Score 並且比 TextCNN 高出 13%。;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 correspond ing transcripts via Kaldi speech recognition. In data pre-processing, transcripts are processed to remove stopwords, split into segments, and assign labels manu ally. 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.
Appears in Collections:	[Graduate Institute of Computer Science and Information Engineering] Electronic Thesis & Dissertation

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