Speaker Diarization with LSTM

TL;DR

This paper introduces a novel speaker diarization system using LSTM-based d-vector embeddings combined with non-parametric clustering, achieving state-of-the-art results and outperforming traditional i-vector methods.

Contribution

It presents the first integration of LSTM-based d-vectors with non-parametric clustering for improved speaker diarization performance.

Findings

Achieved 12.0% DER on NIST SRE 2000 CALLHOME

Outperformed traditional i-vector based systems

Demonstrated effectiveness with out-of-domain training data

Abstract

For many years, i-vector based audio embedding techniques were the dominant approach for speaker verification and speaker diarization applications. However, mirroring the rise of deep learning in various domains, neural network based audio embeddings, also known as d-vectors, have consistently demonstrated superior speaker verification performance. In this paper, we build on the success of d-vector based speaker verification systems to develop a new d-vector based approach to speaker diarization. Specifically, we combine LSTM-based d-vector audio embeddings with recent work in non-parametric clustering to obtain a state-of-the-art speaker diarization system. Our system is evaluated on three standard public datasets, suggesting that d-vector based diarization systems offer significant advantages over traditional i-vector based systems. We achieved a 12.0% diarization error rate on NIST SRE 2000 CALLHOME, while our model is trained with out-of-domain data from voice search logs.