Robust Phoneme Recognition with Little Data

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

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Christopher Dane Shulby
  • Institute of Mathematical and Computer Sciences - University of Sao Paulo, Brazil
  • Samsung SIDI Institute, São Paulo, Brazil
Martha Dais Ferreira
  • Institute of Mathematical and Computer Sciences - University of Sao Paulo, Brazil
Rodrigo F. de Mello
  • Institute of Mathematical and Computer Sciences - University of Sao Paulo, Brazil
Sandra Maria Aluisio
  • Institute of Mathematical and Computer Sciences - University of Sao Paulo, Brazil


We would like to thank our NILC colleagues for their input and support. A great thanks goes to CEMAI. It was only possible to run these experiments thanks to the Euler super-computer cluster at the ICMC - University of Sao Paulo.

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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)


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.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Speech recognition
  • feature extraction
  • acoustic modeling
  • phoneme recognition
  • statistical learning theory


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