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Multiple Dimension Levenshtein Edit Distance Calculations for Evaluating Automatic Speech Recognition Systems During Periods of Overlapping Speech


Jonathan G. Fiscus, Jerome G. Ajot, Nicolas Radde, Christophe Laprun


Since 1987, the National Institute of Standards and Technology has been providing evaluation infrastructure for the Automatic Speech Recognition (ASR), and more recently referred to as the Speech-To-Text (STT), research community. From the first efforts in the Resource Management domain to the present research, the NIST SCoring ToolKit (SCTK) has formed the tool set for system developers to make continued progress in many domains; Wall Street Journal, Conversational Telephone Speech (CTS), Broadcast News (BN), and Meetings (MTG) to name a few. For these domains, the community agreed to declared sections of simultaneous speech as 'not scoreable'. While this had minor impact on most of these domains, the highly interactive nature of Meeting speech rendered a very large fraction of the test material not scoreable. This paper documents a multi-dimensional extension of the Dynamic Programming solution to Levenshtein Edit Distance calculations capable of evaluating STT systems during periods of overlapping, simultaneous speech.


Automating Speech Recognition, Speech-To-Text, Levenshtein Distance, Multidimensional Search
Created August 26, 2016, Updated January 27, 2020