End-to-End Neural Speaker Diarization with Permutation-Free Objectives (1909.05952v1)

Abstract: In this paper, we propose a novel end-to-end neural-network-based speaker diarization method. Unlike most existing methods, our proposed method does not have separate modules for extraction and clustering of speaker representations. Instead, our model has a single neural network that directly outputs speaker diarization results. To realize such a model, we formulate the speaker diarization problem as a multi-label classification problem, and introduces a permutation-free objective function to directly minimize diarization errors without being suffered from the speaker-label permutation problem. Besides its end-to-end simplicity, the proposed method also benefits from being able to explicitly handle overlapping speech during training and inference. Because of the benefit, our model can be easily trained/adapted with real-recorded multi-speaker conversations just by feeding the corresponding multi-speaker segment labels. We evaluated the proposed method on simulated speech mixtures. The proposed method achieved diarization error rate of 12.28%, while a conventional clustering-based system produced diarization error rate of 28.77%. Furthermore, the domain adaptation with real-recorded speech provided 25.6% relative improvement on the CALLHOME dataset. Our source code is available online at https://github.com/hitachi-speech/EEND.

An Analysis of "End-to-End Neural Speaker Diarization with Permutation-Free Objectives"

This paper presents a sophisticated approach to speaker diarization by proposing an end-to-end neural network model that addresses the complexities of speaker-label permutation and overlaps in speech. Traditional speaker diarization systems typically involve a multi-stage process, comprising the extraction and clustering of speaker representations. These systems face challenges, particularly when dealing with overlapping speech and optimizing for diarization errors due to their sequential and unsupervised nature.

The authors introduce a method that encapsulates the entire diarization task within a single neural network framework. This approach is innovative as it transforms the problem into a multi-label classification task, where the network directly outputs the diarization results, thereby eliminating the need for separate clustering and representation modules. The core of this simplification lies in the introduction of permutation-free objective functions, specifically Permutation Invariant Training (PIT) and Deep Clustering (DPCL) losses, which circumvent the speaker-label permutation problem prevalent in overlapping speech scenarios.

The paper highlights significant empirical results garnered from simulated speech mixtures, demonstrating that the proposed method reduces the diarization error rate to 12.28%, in contrast to the 28.77% error rate achieved by conventional clustering methods using x-vectors. Furthermore, domain adaptation using real-world speech datasets provided a relative improvement of 25.6% on the CALLHOME dataset, indicating the model's adaptability and robustness in varied real-world environments.

An exemplar of the paper's contributions is the ability of the model to handle overlapping speech segments explicitly. This capability not only simplifies the architecture but also enhances performance in challenging multi-speaker situations, which are often problematic for traditional methods that assume one speaker per segment. Additionally, by training the model directly on real multi-speaker conversations, the method aligns more closely with actual application scenarios, enhancing its practical utility.

The theoretical implications of this paper are significant. The reformulation of speaker diarization as a multi-label classification task, coupled with permutation-free objectives, marks a departure from traditional diarization strategies. This paradigm shift could inspire further research into end-to-end models for various sequence-labeling tasks, potentially leading to advances in areas such as audio event detection and real-time speech processing applications. Practically, the reduced complexity in system design and the heightened accuracy in diarization imply that real-time applications, such as automated transcription services and interactive voice systems, could see marked improvements.

In examining potential future developments, the integration of more complex neural architectures and training techniques, such as attention mechanisms or transformers, could further improve the model's performance and adaptability across diverse datasets. Moreover, the continuous growth in computational resources and the availability of extensive datasets suggest that training on larger and more diverse datasets could address current limitations and enhance model generalizability.

In conclusion, this paper offers a compelling solution to the inherent challenges in speaker diarization, providing a framework that is both practically effective and theoretically robust. The proposed end-to-end model stands out for its simplicity and enhanced performance, promising a significant impact on future developments in speaker diarization and related fields in audio processing.