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ANTONIO GENTILE

A Study of Perceptron Mapping Capability to Design Speech Event Detectors

  • Autori: SINISCALCHI, SM; CLEMENTS, MA; GENTILE, A; VASSALLO, G; SORBELLO, F
  • Anno di pubblicazione: 2006
  • Tipologia: eedings
  • Parole Chiave: Speech; Speech; recognition; speech segmentation
  • OA Link: http://hdl.handle.net/10447/34281

Abstract

Event detection is a fundamental yet critical component in automatic speech recognition (ASR) systems that attempt to extract knowledge-based features at the front-end level. In this context, it is common practice to design the detectors inside well-known frameworks based on artificial neural network (ANN) or support vector machine (SVM). In the case of ANN, speech scientists often design their detector architecture relying on conventional feed-forward multi-layer perceptron (MLP) with sigmoidal activation function. The aim of this paper is to introduce other ANN architectures inside the context of detection-based ASR. In particular, a bank of feed-forward MLPs using sinusoidal activation functions is set up to address the event detection problem. Experimental results demonstrate the effectiveness of this ANN design for speech attribute detectors.