Broad Phoneme Classification Using Signal Based Features

Improving the Robustness of Phoneme Classification Using Hybrid Features

This work is concerned with improving the robustness of phoneme classification to additive noise with hybrid features using support vector machines (SVMs). In particular, the cepstral features are combined with local energy features of acoustic waveform segments to form a hybrid representation. The local energy features are taken into account separately in the SVM kernel, and a simple subtracti...

Evaluating Features for Broad Species Based Classification of Bird Observations Using Dual-polarized Doppler Weather Radar

Unsupervised Phoneme Segmentation Using Transformed Cepstrum Features

One of the basic problems in speech engineering is phoneme segmentation, that is, to divide a speech stream into a string of phonemes. Automatic Speech Recognition (ASR) models often require reliable phoneme segmentation in the initial training phase, and Text-to-Speech (TTS) systems need a large speech database with correct phoneme segmentation information for improving the performance. Human ...

Automatic Phoneme Segmentation Using Auditory Attention Features

Segmentation of speech into phonemes is beneficial for many spoken language processing applications. Here, a novel method which uses auditory attention features for detecting phoneme boundaries from acoustic signal is proposed. The auditory attention model can successfully detect salient audio events/sounds in an acoustic scene by capturing changes that make such salient events perceptually dif...

Phoneme Classification Using Kernel Principal Component Analysis

A substantial number of linear and nonlinear feature space transformation methods have been proposed in recent years. Using the so-called ”kernel-idea” well-known linear techniques such as Principal Component Analysis(PCA), Linear Discriminant Analysis(LDA) and Independent Component Analysis(ICA) can be non-linearized in a general way. The aim of this paper here is twofold. First, we describe t...