Machine Learning-based Silence Detection in Call Center Telephone Conversations

Iheme L. O., Ozan S., Akagunduz E.

International Conference on Artificial Intelligence and Data Processing (IDAP), Malatya, Türkiye, 21 - 22 Eylül 2019 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/idap.2019.8875958
  • Basıldığı Şehir: Malatya
  • Basıldığı Ülke: Türkiye
  • Anahtar Kelimeler: Voice Activity Detection, Bag of Audio Words, MFCC, Clustering, Call Center, VOICE ACTIVITY DETECTION
  • Orta Doğu Teknik Üniversitesi Adresli: Hayır

Özet

This study presents the development of a voice activity detection (VAD) system tested on call center telephony data obtained from our local site. The concept of bag of audio words (BoAW) combined with a naive Bayes classifier was applied to achieve the task. It was formulated as a binary classification problem with speech as the positive class and silence/background noise as the negative class. All the processing was performed on the Mel-frequency cepstral coefficients (MFCCs) extracted from the audio recordings. The results which are presented as accuracy score and receiver operating characteristics (ROC) indicate an excellent performance of the developed model. The system is to be deployed within our call center to aid data analysis and improve overall efficiency of the center.