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End-to-end Keyword Spotting using Xception-1d

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Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
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Iván Vallés-Pérez, Juan Gómez-Sanchis, Marcelino Martínez-Sober, Joan Vila-Francés, Antonio J. Serrano-López, Emilio Soria-Olivas

Computation and Language · Submitted 9 Oct 2021

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim In this work we show how we achieved state of the art results in the Keyword Spotting field by adapting and tweaking the Xception algorithm, which achieved outstanding results in several computer vision tasks.

how it is stated · asserted 1.00 read the record →

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arXiv ID2110.07498 (version 1)
Pith ID3YLIVYPU, Pith's permanent record of this paper and its review
CitationAntonio J. Serrano-L\'opez, Emilio Soria-Olivas, Iv\'an Vall\'es-P\'erez, Joan Vila-Franc\'es, Juan G\'omez-Sanchis, Marcelino Mart\'inez-Sober. "End-to-end Keyword Spotting using Xception-1d." arXiv:2110.07498 (2021). https://pith.science/paper/3YLIVYPU
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 2,944 input tokens.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.06 · human participants 0.17 · controlled comparison 0.35 · randomized assignment 0.01 · states quantitative result 0.99 · compares with prior results 0.81 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

The field of conversational agents is growing fast and there is an increasing need for algorithms that enhance natural interaction. In this work we show how we achieved state of the art results in the Keyword Spotting field by adapting and tweaking the Xception algorithm, which achieved outstanding results in several computer vision tasks. We obtained about 96\% accuracy when classifying audio clips belonging to 35 different categories, beating human annotation at the most complex tasks proposed.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 The field of conversational agents is growing fast and there is an increasing need for algorithms that enhance natural interaction. background 1.00 0.04 0.02 asserted 1.00
s1 In this work we show how we achieved state of the art results in the Keyword Spotting field by adapting and tweaking the Xception algorithm, which achieved outstanding results in several computer vision tasks. contribution 0.86 0.94 0.46 asserted 1.00
s2 We obtained about 96\% accuracy when classifying audio clips belonging to 35 different categories, beating human annotation at the most complex tasks proposed. result 1.00 0.96 0.66 asserted 1.00

What kind of paper the classifier reports this is: method or tool 0.90 · evidence from observation or experiment 0.97.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

The field of conversational agents is growing fast and there is an increasing need for algorithms that enhance natural interaction. In this work we show how we achieved state of the art results in the Keyword Spotting field by adapting and tweaking the Xception algorithm, which achieved outstanding results in several computer vision tasks. We obtained about 96\% accuracy when classifying audio clips belonging to 35 different categories, beating human annotation at the most complex tasks proposed.

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What this paper does with the arXiv papers it cites

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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