Application of EalphaNets to Feature Recognition of Articulation Manner in Knowledge-Based Automatic Speech Recognition

Sabato Marco Siniscalchi, Giorgio Vassallo, Antonio Gentile, Filippo Sorbello, Sabato M. Siniscalchi, Mark A. Clements, Jinyu Li, Giovanni Pilato

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Abstract

Speech recognition has become common in many application domains. Incorporating acoustic-phonetic knowledge into Automatic Speech Recognition (ASR) systems design has been proven a viable approach to rise ASR accuracy. Manner of articulation attributes such as vowel, stop, fricative, approximant, nasal, and silence are examples of such knowledge. Neural networks have already been used successfully as detectors for manner of articulation attributes starting from representations of speech signal frames. In this paper, a set of six detectors for the above mentioned attributes is designed based on the E-αNet model of neural networks. This model was chosen for its capability to learn hidden activation functions that results in better generalization properties. Experimental set-up and results are presented that show an average 3.5% improvement over a baseline neural network implementation
Lingua originaleEnglish
Titolo della pubblicazione ospiteLecture Notes in Computer Science
Pagine140-146
Numero di pagine7
Stato di pubblicazionePublished - 2006

Serie di pubblicazioni

NomeLECTURE NOTES IN COMPUTER SCIENCE

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All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cita questo

Siniscalchi, S. M., Vassallo, G., Gentile, A., Sorbello, F., Siniscalchi, S. M., Clements, M. A., Li, J., & Pilato, G. (2006). Application of EalphaNets to Feature Recognition of Articulation Manner in Knowledge-Based Automatic Speech Recognition. In Lecture Notes in Computer Science (pagg. 140-146). (LECTURE NOTES IN COMPUTER SCIENCE).