AbstructWe have compared the performance of four approaches for automatic language identification of speech utterances: Gaussian mixture model (GMM) classification; single-language phone recognition followed by languagedependent, interpolated n-gram language modeling (PRLM); parallel PRLM, which uses multiple single-language phone recognizers, each trained in a different language; and languaged...

Statistical classifiers operate on features that generally include both useful and useless information. These two types of information are difficult to separate in the feature domain. Recently, a new paradigm based on a Latent Factor Analysis (LFA) proposed a model decomposition into usefull and useless components. This method was successfully applied to speaker and language recognition tasks. ...

Snoring is caused by the narrowing of the upper airway and it is excited by different locations within the upper airways. This irregularity could lead to the presence of Obstructive Sleep Apnea Syndrome (OSAS). Diagnosis of OSAS could therefore be made by snoring sound analysis. This paper proposes the novel method to automatically classify snoring sounds by their excitation locations for ComPa...

The application of neural network and convolutional neural network (CNN) architectures is explored for the tasks of voicing classification (classifying frames as being either non-speech, unvoiced, or voiced) and voice activity detection (VAD) of visual speech. Experiments are conducted for both speaker dependent and speaker independent scenarios. A Gaussian mixture model (GMM) baseline system i...

Increased water scarcity necessitates the implementation of water-conserving irrigation management practices to sustain crop production, especially in water-limited areas. Field experiments were conducted evaluate midday leaf potential (LWP) bell pepper (Capsicum annuum L. var. annuum) irrigated differentially using surface drip (DI) and subsurface (SDI) systems during 2016 2017 growing seasons...

This paper addresses the "one-to-many" mapping problem in Voice Conversion (VC) by exploring source-to-target mappings in GMM-based spectral transformation. Specifically, we examine differences using source-only versus joint source/target information in the classification stage of transformation, effectively illustrating a "one-to-many effect" in the traditional acoustically-based GMM. We propo...

Language recognition with support vector machines and shifteddelta cepstral features has been an excellent performer in NISTsponsored language evaluation for many years. A novel improvement of this method has been the introduction of hybrid SVM/GMM systems. These systems use GMM supervectors as an SVM expansion for classification. In prior work, methods for scoring SVM/GMM systems have been int...

The recently proposed “generalized min-max” (GMM) kernel [9] can be efficiently linearized, with direct applications in large-scale statistical learning and fast near neighbor search. The linearized GMM kernel was extensively compared in [9] with linearized radial basis function (RBF) kernel. On a large number of classification tasks, the tuning-free GMM kernel performs (surprisingly) well comp...