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- W4386977307 abstract "Speech-to-Speech and Speech-to-Text translation are currently dynamic areas of research. To contribute to these fields, we present SpeechAlign, a framework to evaluate the underexplored field of source-target alignment in speech models. Our framework has two core components. First, to tackle the absence of suitable evaluation datasets, we introduce the Speech Gold Alignment dataset, built upon a English-German text translation gold alignment dataset. Secondly, we introduce two novel metrics, Speech Alignment Error Rate (SAER) and Time-weighted Speech Alignment Error Rate (TW-SAER), to evaluate alignment quality in speech models. By publishing SpeechAlign we provide an accessible evaluation framework for model assessment, and we employ it to benchmark open-source Speech Translation models." @default.
- W4386977307 created "2023-09-23" @default.
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- W4386977307 date "2023-09-20" @default.
- W4386977307 modified "2023-09-26" @default.
- W4386977307 title "SpeechAlign: a Framework for Speech Translation Alignment Evaluation" @default.
- W4386977307 doi "https://doi.org/10.48550/arxiv.2309.11585" @default.
- W4386977307 hasPublicationYear "2023" @default.
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