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Symbolic Weighted Language Models, Quantitative Parsing and Automated Music Transcription

Abstract : We study several classes of symbolic weighted formalisms: automata (swA), transducers (swT) and visibly pushdown extensions (swVPA, swVPT). They combine the respective extensions of their symbolic and weighted counterparts, allowing a quantitative evaluation of words over a large or infinite input alphabet. We present properties of closure by composition, the computation of transducer-defined distances between nested words and languages, as well as a PTIME 1-best search algorithm for swVPA. These results are applied to solve in PTIME a variant of parsing over infinite alphabets. We illustrate this approach with a motivating use case in automated music transcription.
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Contributor : Lydia Rodriguez--de la Nava Connect in order to contact the contributor
Submitted on : Wednesday, April 20, 2022 - 5:21:14 PM
Last modification on : Wednesday, June 8, 2022 - 12:50:03 PM


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  • HAL Id : hal-03647675, version 1


Florent Jacquemard, Lydia Rodriguez--de La Nava. Symbolic Weighted Language Models, Quantitative Parsing and Automated Music Transcription. CIAA 2022 - International Conference on Implementation and Application of Automata, Jun 2022, Rouen, France. ⟨hal-03647675v1⟩



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