Modeling Overlapped Speech with Shuffles

Matthew Wiesner, Samuele Cornell, Alexander Polok, ★ Sanjeev Khudanpur ...Full author list in the original paper.

Carnegie Mellon University +2 · arXiv

Affiliations: Brno University of Technology, Carnegie Mellon University, Johns Hopkins University

Abstract

We propose to model parallel streams of data, such as overlapped speech, using shuffles. Specifically, this paper shows how the shuffle product and partial order finite-state automata (FSAs) can be used for alignment and speaker-attributed transcription of overlapped speech. We train using the total score on these FSAs as a loss function, marginalizing over all possible serializations of overlapping sequences at subword, word, and phrase levels. To reduce graph size, we impose temporal constraints by constructing partial order FSAs. We address speaker attribution by modeling (token, speaker) tuples directly. Viterbi alignment through the shuffle product FSA directly enables one-pass alignment. We evaluate performance on synthetic LibriSpeech overlaps. To our knowledge, this is the first algorithm that enables single-pass alignment of multi-talker recordings. All algorithms are implemented using k2 / Icefall.

The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.

Research summary

The main contribution of this paper is the introduction of a novel algorithm for the single-pass alignment of multi-talker recordings using shuffle products and partial order FSAs. This work represents a significant advancement in the field of speech processing, particularly in addressing the challenges posed by overlapped speech, and has the potential to influence future research and applications in audio processing.

Methodology

The methodology presented in this paper is innovative in its application of shuffle products and partial order finite-state automata (FSAs) for modeling overlapped speech. The authors effectively leverage these mathematical constructs to create a framework for alignment and transcription of multi-talker recordings. The approach of using (token, speaker) tuples for speaker attribution is particularly noteworthy, as it directly addresses a significant challenge in the field of speech processing. The imposition of temporal constraints to reduce graph size is a practical consideration that enhances the efficiency of the proposed method.

Results & evaluation

The experiments conducted on synthetic LibriSpeech overlaps provide a solid basis for evaluating the proposed methods. The paper compares the performance of the shuffle product FSA against traditional methods, demonstrating a clear advantage in terms of alignment accuracy. However, the reliance on synthetic data may limit the generalizability of the results to real-world scenarios. The metrics used for evaluation are appropriate, but further validation on diverse datasets would strengthen the findings.

Reproducibility

The paper mentions that all algorithms are implemented using k2 / Icefall, which is a positive aspect for reproducibility. However, the lack of a publicly available code repository or detailed implementation instructions may hinder other researchers from replicating the results. Providing a GitHub repository or similar resource would greatly enhance the reproducibility of the work.

Limitations

One limitation of the study is the use of synthetic data for training and evaluation, which may not fully capture the complexities of real-world overlapped speech scenarios. Additionally, while the proposed method shows promise, the paper does not provide extensive comparisons with other state-of-the-art techniques, which could have offered more context regarding its performance.

Broader impact

The ability to accurately transcribe and attribute overlapped speech has significant implications for various applications, including automated transcription services, assistive technologies for the hearing impaired, and improvements in human-computer interaction. The proposed method could pave the way for advancements in multi-talker speech recognition systems, making them more robust and effective. The main contribution of this paper is the introduction of a novel algorithm for the single-pass alignment of multi-talker recordings using shuffle products and partial order FSAs. This work represents a significant advancement in the field of speech processing, particularly in addressing the challenges posed by overlapped speech, and has the potential to influence future research and applications in audio processing.

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