A Study of Perceptron Mapping Capability to Design Speech Event Detectors

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1 Citazioni (Scopus)


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.
Lingua originaleEnglish
Numero di pagine4
Stato di pubblicazionePublished - 2006

All Science Journal Classification (ASJC) codes

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  • ???subjectarea.asjc.1700.1711???
  • ???subjectarea.asjc.2200.2208???


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