In this paper we present an automated method for the classification of the origin of non-native speakers. The origin of non-native speakers could be identified by a human listener based on the detection of typical pronunciations for each nationality. Thus we suppose the existence of several phoneme sequences that might allow the classification of the origin of non-native speakers. Our new metho...

Deep neural networks (DNNs) are widely utilized for acoustic modeling in speech recognition systems. Through training, DNNs used for phoneme recognition nonlinearly transform the time-frequency representation of a speech signal into a sequence of invariant phonemic categories. However, little is known about how this nonlinear mapping is performed and what its implications are for the classifica...

OBJECTIVE Although brain-computer interfaces (BCIs) can be used in several different ways to restore communication, communicative BCI has not approached the rate or efficiency of natural human speech. Electrocorticography (ECoG) has precise spatiotemporal resolution that enables recording of brain activity distributed over a wide area of cortex, such as during speech production. In this study, ...

By means of full wavelet packet decomposition a redundant set of coefficients is obtained. For signal classification it is convenient to find a subset of these coefficients minimizing the error rate of a classifier. A problem arises because of the computational cost of GA solution. This work presents the parallelization of a genetic algorithm by which it is possible to obtain the best subset of...

This paper reports positive results for classifying imagined phonemes on the basis of EEG signals. Subjects generated in imagination five types of phonemes that differ in their primary manner of vocal articulation during overt speech production (jaw, tongue, nasal, lips and fricative). Naive Bayes and linear discriminant analysis classification methods were applied to EEG signals that were reco...

Linear Disciminant Analysis is in general unable to find the lower-dimensional feature space which maximizes the class discrimination, even if the class distributions can be assumed to be very simple, e.g. Gaussians with identical covariance matrices. In this paper we reformulate the K-class Fisher criterion as a sum of K(K 1)=2 2-class Fisher criteria. This formulation allows to weigh class pa...

This paper presents a method of augmenting shifted-delta cepstral coefficients (SDCCs) with the classification outputs of an array of support vector machines (SVMs) trained to detect a set of manner and place features on telephone speech. The SVM array allows for broad phoneme classification, and when this information is concatenated with SDCCs to form a hybrid feature vector for each acoustic ...

This paper encompasses the approaches of segmental modelling and the use of dynamic features in addressing the constraints of the IID assumption in standard HMM. Phonetic features are introduced which capture the transitional dynamics across a phoneme unit via a DCT transformation of a variable length segment. Alongside this, the use of a hybrid phoneme model is proposed. Classification experim...

This paper proposes a novel technique to exploit discriminative models with subclasses for speech recognition. Speech recognition using discriminative models has attracted much attention in the past decade. However, most discriminative models are still based on tree clustering results of HMM states. On the contrary, our proposed method, referred to as subclass AdaBoost, jointly selects optimal ...

Abstract. This paper studies the application of automatic phoneme classification to the computer-aided training of the speech and hearing handicapped. In particular, we focus on how efficiently discriminant analysis can reduce the number of features and increase classification performance. A nonlinear counterpart of Linear Discriminant Analysis, which is a general purpose class specific feature...