Speaker detection in the wild: Lessons learned from JSALT 2019

TL;DR

This paper investigates speaker detection in challenging real-world scenarios, emphasizing the importance of diarization as a crucial step for improving detection accuracy across diverse conditions.

Contribution

It demonstrates that incorporating diarization as a preliminary stage significantly enhances speaker detection performance in adverse environments.

Findings

Diarization improves speaker detection accuracy.

Effective handling of noisy signals is crucial.

Domain mismatch impacts detection performance.

Abstract

This paper presents the problems and solutions addressed at the JSALT workshop when using a single microphone for speaker detection in adverse scenarios. The main focus was to tackle a wide range of conditions that go from meetings to wild speech. We describe the research threads we explored and a set of modules that was successful for these scenarios. The ultimate goal was to explore speaker detection; but our first finding was that an effective diarization improves detection, and not having a diarization stage impoverishes the performance. All the different configurations of our research agree on this fact and follow a main backbone that includes diarization as a previous stage. With this backbone, we analyzed the following problems: voice activity detection, how to deal with noisy signals, domain mismatch, how to improve the clustering; and the overall impact of previous stages in the final speaker detection. In this paper, we show partial results for speaker diarizarion to have a better understanding of the problem and we present the final results for speaker detection.