Unsupervised deep feature embeddings for speaker diarization

Rehan AHMAD; Syed ZUBAIR

Makale detayları

Unsupervised deep feature embeddings for speaker diarization

Yazarlar:

Rehan AHMAD | Syed ZUBAIR

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2019 Vol.27 Issue 4, pp.3138-3149

Belge Tipi:

Makale

Yayın Tarihi:

7/2019

Belge Dili:

İngilizce

Document ID (Org No.):

2000733901

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632

Özet:

 

Speaker diarization aims to determine ?who spoke when?? from multispeaker recording environments. In  this paper, we propose to learn a set of high-level feature representations, referred to as feature embeddings, from an  unsupervised deep architecture for speaker diarization. These sets of embeddings are learned through a deep autoencoder  model when trained on mel-frequency cepstral coefficients (MFCCs) of input speech frames. Learned embeddings are then  used in Gaussian mixture model based hierarchical clustering for diarization. The results show that these unsupervised  embeddings are better compared to MFCCs in reducing the diarization error rate. Experiments conducted on the popular  subset of the AMI meeting corpus consisting of 5.4 h of recordings show that the new embeddings decrease the average  diarization error rate by 2.96%. However, for individual recordings, maximum improvement of 8.05% is acquired.

Makale detayları

Unsupervised deep feature embeddings for speaker diarization

Yazarlar: Rehan AHMAD | Syed ZUBAIR

Yayıncı: TUBITAK

Dergi: Turkish Journal of Electrical Engineering and Computer Science 2019 Vol.27 Issue 4, pp.3138-3149

Belge Tipi: Makale

Yayın Tarihi: 7/2019

Belge Dili: İngilizce

Document ID (Org No.): 2000733901

Sıklık: İki Aylık

eISSN: 1303-6203

pISSN: 1300-0632

Sayı

Yazarlar:

Rehan AHMAD | Syed ZUBAIR

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2019 Vol.27 Issue 4, pp.3138-3149

Belge Tipi:

Makale

Yayın Tarihi:

7/2019

Belge Dili:

İngilizce

Document ID (Org No.):

2000733901

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632