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Documents authored by Ferreira, Martha Dais

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DOI: 10.4230/OASIcs.SLATE.2019.4
Robust Phoneme Recognition with Little Data

Authors: Christopher Dane Shulby, Martha Dais Ferreira, Rodrigo F. de Mello, and Sandra Maria Aluisio

Published in: OASIcs, Volume 74, 8th Symposium on Languages, Applications and Technologies (SLATE 2019)

Abstract
A common belief in the community is that deep learning requires large datasets to be effective. We show that with careful parameter selection, deep feature extraction can be applied even to small datasets.We also explore exactly how much data is necessary to guarantee learning by convergence analysis and calculating the shattering coefficient for the algorithms used. Another problem is that state-of-the-art results are rarely reproducible because they use proprietary datasets, pretrained networks and/or weight initializations from other larger networks. We present a two-fold novelty for this situation where a carefully designed CNN architecture, together with a knowledge-driven classifier achieves nearly state-of-the-art phoneme recognition results with absolutely no pretraining or external weight initialization. We also beat the best replication study of the state of the art with a 28% FER. More importantly, we are able to achieve transparent, reproducible frame-level accuracy and, additionally, perform a convergence analysis to show the generalization capacity of the model providing statistical evidence that our results are not obtained by chance. Furthermore, we show how algorithms with strong learning guarantees can not only benefit from raw data extraction but contribute with more robust results.
Cite as

Christopher Dane Shulby, Martha Dais Ferreira, Rodrigo F. de Mello, and Sandra Maria Aluisio. Robust Phoneme Recognition with Little Data. In 8th Symposium on Languages, Applications and Technologies (SLATE 2019). Open Access Series in Informatics (OASIcs), Volume 74, pp. 4:1-4:11, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)

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@InProceedings{shulby_et_al:OASIcs.SLATE.2019.4,
  author =	{Shulby, Christopher Dane and Ferreira, Martha Dais and de Mello, Rodrigo F. and Aluisio, Sandra Maria},
  title =	{{Robust Phoneme Recognition with Little Data}},
  booktitle =	{8th Symposium on Languages, Applications and Technologies (SLATE 2019)},
  pages =	{4:1--4:11},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-114-6},
  ISSN =	{2190-6807},
  year =	{2019},
  volume =	{74},
  editor =	{Rodrigues, Ricardo and Janou\v{s}ek, Jan and Ferreira, Lu{\'\i}s and Coheur, Lu{\'\i}sa and Batista, Fernando and Gon\c{c}alo Oliveira, Hugo},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.SLATE.2019.4},
  URN =		{urn:nbn:de:0030-drops-108715},
  doi =		{10.4230/OASIcs.SLATE.2019.4},
  annote =	{Keywords: feature extraction, acoustic modeling, phoneme recognition, statistical learning theory}
}
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