Sparse representation based classification has gained popularity with geospatial image analysis in general and hyperspectral image analysis in particular. A central idea with such classification approaches is that a test pixel (spectral reflectance vector)...

Due to the benefits and limitation of different remote sensing sensors, fusion of the features from multiple sensors, such as hyperspectral and light detection and ranging (LiDAR) is an effective method for land cover mapping. In this paper, we propose a ...

Natural language understanding and dialogue policy learning are both essential in conversational systems that predict the next system actions in response to a current user utterance. Conventional approaches aggregate separate models of natural language ...

Systems based on i-vectors represent the current state-of-the-art in text-independent speaker recognition. In this work we introduce a new compact representation of a speech segment, similar to the speaker factors of Joint Factor Analysis (JFA) and to i-...

TristouNet is a neural network architecture based on Long Short-Term Memory recurrent networks, meant to project speech sequences into a fixed-dimensional euclidean space. Thanks to the triplet loss paradigm used for training, the resulting sequence ...

The performance of speech emotion classifiers greatly degrade when the training conditions do not match the testing conditions. This problem is observed in cross-corpora evaluations, even when the corpora are similar. The lack of generalization is ...

Continuous prediction of dimensional emotions (e.g. arousal and valence) has attracted increasing research interest recently. When processing emotional speech signals, phonetic features have been rarely used due to the assumption that phonetic variability ...

Conventional feature-based classification methods do not apply well to automatic recognition of speech emotions, mostly because the precise set of spectral and prosodic features that is required to identify the emotional state of a speaker has not been ...

This paper addresses the problem of speech emotion recognition from movie audio tracks. The recently collected Acted Facial Expression in the Wild 5.0 database is used. The aim is to discriminate among angry, happy, and neutral. We extract a relatively ...

Emotion representations are psychological constructs for modelling, analysing, and recognising emotion, being one essential element of affect. Due to its complexity, the boundaries between different emotion concepts are often fuzzy, which is also ...

Albeit recent progress in speaker verification generates powerful models, malicious attacks in the form of spoofed speech, are generally not coped with. Recent results in ASVSpoof2015 and BTAS2016 challenges indicate that spoof-aware features are a ...

Training neural network acoustic models on limited quantities of data is a challenging task. A number of techniques have been proposed to improve generalisation. This paper investigates one such technique called stimulated training. It enables standard ...

Seeking effective measures to characterize the chaotic patterns of EEG signals for seizure diagnosis is a long-term endeavor in the literature. We propose to count the number of zero-crossing (ZC) points on Poincaré surface as a feature when the ...

Statistically-optimal Linear Discriminant Analysis (LDA) is formulated as a maximization that involves the nominal statistics of the classes to be discriminated. In practice, however, these nominal statistics are unknown and estimated from a collection of ...

In this paper, single-target tracking using radar measurements is addressed. Recently, algorithms based on Bernoulli random finite sets have proved efficient in a cluttered environment. However, in Bayesian approaches, the choice of the motion model ...

Non-cosmic, non-Gaussian disturbances known as “glitches”, show up in gravitational-wave data of the Advanced Laser Interferometer Gravitational-wave Observatory, or aLIGO. In this paper, we propose a deep multi-view convolutional neural ...

An effective novel algorithm to reduce the dimensionality of labeled proportional data is presented which uses an optimal linear projection to project the data into a low-dimensional space. Assuming that each class of the projected data is generated by a ...

Component Analysis (CA) comprises of statistical techniques that decompose signals into appropriate latent components, relevant to a task-at-hand (e.g., clustering, segmentation, classification). Recently, an explosion of research in CA has been witnessed,...

In this paper, we propose to apply ensemble learning methods on neural networks to improve the performance of speech emotion recognition tasks. The basic idea is to first divide unbalanced data set into balanced subsets and then combine the predictions of ...

Automatically assessing emotional valence in human speech has historically been a difficult task for machine learning algorithms. The subtle changes in the voice of the speaker that are indicative of positive or negative emotional states are often “...