Extended Graph Temporal Classification for Multi-Speaker End-to-End ASR


Graph-based temporal classification (GTC), a generalized form of the connectionist temporal classification loss, was recently proposed to improve automatic speech recognition (ASR) systems using graph-based supervision. For example, GTC was first used to encode an N-best list of pseudo-label sequences into a graph for semi-supervised learning. In this paper, we propose an extension of GTC to model the posteriors of both labels and label transitions by a neural network, which can be applied to a wider range of tasks. As an example application, we use the extended GTC (GTC-e) for the multi-speaker speech recognition task. The transcriptions and speaker information of multi-speaker speech are represented by a graph, where the speaker information is associated with the transitions and ASR outputs with the nodes. Using GTC-e, multi-speaker ASR modelling becomes very similar to single-speaker ASR modeling, in that tokens by multiple speakers are recognized as a single merged sequence in chronological order. For evaluation, we perform experiments on a simulated multi-speaker speech dataset derived from LibriSpeech, obtaining promising results with performance close to classical benchmarks for the task.


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  •  Chang, X., Moritz, N., Hori, T., Watanabe, S., Le Roux, J., "Extended Graph Temporal Classification for Multi-Speaker End-to-End ASR", arXiv, DOI: 10.48550/​arXiv.2203.00232, March 2022.
    BibTeX arXiv
    • @article{Chang2022mar,
    • author = {Chang, Xuankai and Moritz, Niko and Hori, Takaaki and Watanabe, Shinji and Le Roux, Jonathan},
    • title = {Extended Graph Temporal Classification for Multi-Speaker End-to-End ASR},
    • journal = {arXiv},
    • year = 2022,
    • month = mar,
    • doi = {10.48550/arXiv.2203.00232},
    • url = {}
    • }