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The Speed Submission to DIHARD II: Contributions & Lessons Learned

Nicholas Evans, Sébastien Marcel

This paper describes the speaker diarization systems developed for the Second DIHARD Speech Diarization Challenge (DIHARD II) by the Speed team. Besides describing the system, which considerably outperformed the challenge baselines, we also focus on the lessons learned from numerous approaches that we tried for single and multi-channel systems. We present several components of our diarization system, including categorization of domains, speech enhancement, speech activity detection, speaker embeddings, clustering methods, resegmentation, and system fusion. We analyze and discuss the effect of each such component on the overall diarization performance within the realistic settings of the challenge.

Idiap2019

Low-latency speaker spotting with online diarization and detection

Nicholas Evans, Sébastien Marcel

This paper introduces a new task termed low-latency speaker spotting (LLSS). Related to security and intelligence applications, the task involves the detection, as soon as possible, of known speakers within multi-speaker audio streams. The paper describes differences to the established fields of speaker diarization and automatic speaker verification and proposes a new protocol and metrics to support exploration of LLSS. These can be used together with an existing, publicly available database to assess the performance of LLSS solutions also proposed in the paper. They combine online diarization and speaker detection systems. Diarization systems include a naive, over-segmentation approach and fully-fledged online diarization using segmental i-vectors. Speaker detection is performed using Gaussian mixture models, i-vectors or neural speaker embeddings. Metrics reflect different approaches to characterise latency in addition to detection performance. The relative performance of each solution is dependent on latency. When higher latency is admissible, i-vector solutions perform well; embeddings excel when latency must be kept to a minimum. With a need to improve the reliability of online diarization and detection, the proposed LLSS framework provides a vehicle to fuel future research in both areas. In this respect, we embrace a reproducible research policy; results can be readily reproduced using publicly available resources and open source codes.

2018

Biometrics systems under spoofing attack: an evaluation methodology and lessons learned

Nicholas Evans, Sébastien Marcel

Biometrics already form a significant component of current and emerging identification technologies. Biometrics systems aim to determine or verify the identity of an individual from their behavioral and/or biological characteristics. Despite significant progress, some biometric systems fail to meet the multitude of stringent security and robustness requirements to support their deployment in some practical scenarios. Among current concerns are vulnerabilities to spoofing?persons who masquerade as others to gain illegitimate accesses to protected data, services, or facilities. While the study of spoofing, or rather antispoofing, has attracted growing interest in recent years, the problem is far from being solved and will require far greater attention in the coming years. This tutorial article presents an introduction to spoofing and antispoofing research. It describes the vulnerabilities, presents an evaluation methodology for the assessment of spoofing and countermeasures, and outlines research priorities for the future.

2015