Speaker Diarization using Deep Recurrent Convolutional Neural Networks for Speaker Embeddings

Abstract

In this paper we propose a new method of speaker diarization that employs a deep learning architecture to learn speaker embeddings. In contrast to the traditional approaches that build their speaker embeddings using manually hand-crafted spectral features, we propose to train for this purpose a recurrent convolutional neural network applied directly on magnitude spectrograms. To compare our approach with the state of the art, we collect and release for the public an additional dataset of over 6 hours of fully annotated broadcast material. The results of our evaluation on the new dataset and three other benchmark datasets show that our proposed method significantly outperforms the competitors and reduces diarization error rate by a large margin of over 30% with respect to the baseline.

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Cited by in corpus (10)

• Improving Universal Sound Separation Using Sound Classification
• Combination of Deep Speaker Embeddings for Diarisation
• Self-supervised Representation Learning With Path Integral Clustering For Speaker Diarization
• Improved Large-margin Softmax Loss for Speaker Diarisation
• A study of semi-supervised speaker diarization system using gan mixture model
• A Thousand Words are Worth More Than One Recording: NLP Based Speaker Change Point Detection
• Speaker diarisation using 2D self-attentive combination of embeddings
• Speaker Diarization: Using Recurrent Neural Networks
• X-Vectors with Multi-Scale Aggregation for Speaker Diarization
• SocialML: machine learning for social media video creators