Continuous Estimation of Emotions in Speech by Dynamic Cooperative Speaker Models

Automatic emotion recognition from speech has been recently focused on the prediction of time-continuous dimensions (e.g., arousal and valence) of spontaneous and realistic expressions of emotion, as found in real-life interactions. However, the automatic prediction of such emotions poses several challenges, such as the subjectivity found in the definition of a gold standard from a pool of raters and the issue of data scarcity in training models. In this work, we introduce a novel emotion recognition system, based on ensemble of single-speaker-regression-models (SSRMs). The estimation of emotion is provided by combining a subset of the initial pool of SSRMs selecting those that are most concordance among them. The proposed approach allows the addition or removal of speakers from the ensemble without the necessity to re-build the entire machine learning system. The simplicity of this aggregation strategy, coupled with the flexibility assured by the modular architecture, and the promising results obtained on the RECOLA database highlight the potential implications of the proposed method in a real-life scenario and in particular in WEB-based applications

I hear you eat and speak: automatic recognition of eating condition and food type, use-cases, and impact on ASR performance

We propose a new recognition task in the area of computational paralinguistics: automatic recognition of eating conditions in speech, i. e., whether people are eating while speaking, and what they are eating. To this end, we introduce the audio-visual iHEARu-EAT database featuring 1.6 k utterances of 30 subjects (mean age: 26.1 years, standard deviation: 2.66 years, gender balanced, German speakers), six types of food (Apple, Nectarine, Banana, Haribo Smurfs, Biscuit, and Crisps), and read as well as spontaneous speech, which is made publicly available for research purposes. We start with demonstrating that for automatic speech recognition (ASR), it pays off to know whether speakers are eating or not. We also propose automatic classification both by brute-forcing of low-level acoustic features as well as higher-level features related to intelligibility, obtained from an Automatic Speech Recogniser. Prediction of the eating condition was performed with a Support Vector Machine (SVM) classifier employed in a leave-one-speaker-out evaluation framework. Results show that the binary prediction of eating condition (i. e., eating or not eating) can be easily solved independently of the speaking condition; the obtained average recalls are all above 90%. Low-level acoustic features provide the best performance on spontaneous speech, which reaches up to 62.3% average recall for multi-way classification of the eating condition, i. e., discriminating the six types of food, as well as not eating. The early fusion of features related to intelligibility with the brute-forced acoustic feature set improves the performance on read speech, reaching a 66.4% average recall for the multi-way classification task. Analysing features and classifier errors leads to a suitable ordinal scale for eating conditions, on which automatic regression can be performed with up to 56.2% determination coefficient

The ICL-TUM-PASSAU approach for the MediaEval 2015 "affective impact of movies" task

In this paper we describe the Imperial College London, Technische Universitat München and University of Passau (ICL+TUM+PASSAU) team approach to the MediaEval's "Affective Impact of Movies" challenge, which consists in the automatic detection of affective (arousal and valence) and violent content in movie excerpts. In addition to the baseline features, we computed spectral and energy related acoustic features, and the probability of various objects being present in the video. Random Forests, AdaBoost and Support Vector Machines were used as classification methods. Best results show that the dataset is highly challenging for both affect and violence detection tasks, mainly because of issues in inter-rater agreement and data scarcity

Response of methane emissions from wetlands to the Last Glacial Maximum and an idealized Dansgaard-Oeschger climate event: insights from two models of different complexity

The role of different sources and sinks of CH₄ in changes in atmospheric methane ([CH₄]) concentration during the last 100 000 yr is still not fully understood. In particular, the magnitude of the change in wetland CH₄ emissions at the Last Glacial Maximum (LGM) relative to the pre-industrial period (PI), as well as during abrupt climatic warming or Dansgaard–Oeschger (D–O) events of the last glacial period, is largely unconstrained. In the present study, we aim to understand the uncertainties related to the parameterization of the wetland CH₄ emission models relevant to these time periods by using two wetland models of different complexity (SDGVM and ORCHIDEE). These models have been forced by identical climate fields from low-resolution coupled atmosphere–ocean general circulation model (FAMOUS) simulations of these time periods. Both emission models simulate a large decrease in emissions during LGM in comparison to PI consistent with ice core observations and previous modelling studies. The global reduction is much larger in ORCHIDEE than in SDGVM (respectively −67 and −46%), and whilst the differences can be partially explained by different model sensitivities to temperature, the major reason for spatial differences between the models is the inclusion of freezing of soil water in ORCHIDEE and the resultant impact on methanogenesis substrate availability in boreal regions. Besides, a sensitivity test performed with ORCHIDEE in which the methanogenesis substrate sensitivity to the precipitations is modified to be more realistic gives a LGM reduction of −36%. The range of the global LGM decrease is still prone to uncertainty, and here we underline its sensitivity to different process parameterizations. Over the course of an idealized D–O warming, the magnitude of the change in wetland CH₄ emissions simulated by the two models at global scale is very similar at around 15 Tg yr⁻¹, but this is only around 25% of the ice-core measured changes in [CH₄]. The two models do show regional differences in emission sensitivity to climate with much larger magnitudes of northern and southern tropical anomalies in ORCHIDEE. However, the simulated northern and southern tropical anomalies partially compensate each other in both models limiting the net flux change. Future work may need to consider the inclusion of more detailed wetland processes (e.g. linked to permafrost or tropical floodplains), other non-wetland CH₄ sources or different patterns of D–O climate change in order to be able to reconcile emission estimates with the ice-core data for rapid CH₄ events

Fermionic massive modes along cosmic strings

The influence on cosmic string dynamics of fermionic massive bound states propagating in the vortex, and getting their mass only from coupling to the string forming Higgs field, is studied. Such massive fermionic currents are numerically found to exist for a wide range of model parameters and seen to modify drastically the usual string dynamics coming from the zero mode currents alone. In particular, by means of a quantization procedure, a new equation of state describing cosmic strings with any kind of fermionic current, massive or massless, is derived and found to involve, at least, one state parameter per trapped fermion species. This equation of state exhibits transitions from subsonic to supersonic regimes while the massive modes are filled.Comment: 27 pages, 15 figures, uses ReVTeX. Shortened version, accepted for publication in Phys. Rev.

Localisation of massive fermions on the brane

We construct an explicit model to describe fermions confined on a four dimensional brane embedded in a five dimensional anti-de Sitter spacetime. We extend previous works to accommodate massive bound states on the brane and exhibit the transverse structure of the fermionic fields. We estimate analytically and calculate numerically the fermion mass spectrum on the brane, which we show to be discrete. The confinement life-time of the bound states is evaluated, and it is shown that existing constraints can be made compatible with the existence of massive fermions trapped on the brane for durations much longer than the age of the Universe.Comment: 20 pages, LaTeX-RevTex, 15 figures, submitted to PR

Methane emissions from floodplains in the Amazon Basin: challenges in developing a process-based model for global applications

Tropical wetlands are estimated to represent about 50% of the natural wetland methane (CH₄) emissions and explain a large fraction of the observed CH₄ variability on timescales ranging from glacial–interglacial cycles to the currently observed year-to-year variability. Despite their importance, however, tropical wetlands are poorly represented in global models aiming to predict global CH₄ emissions. This publication documents a first step in the development of a process-based model of CH₄ emissions from tropical floodplains for global applications. For this purpose, the LPX-Bern Dynamic Global Vegetation Model (LPX hereafter) was slightly modified to represent floodplain hydrology, vegetation and associated CH₄ emissions. The extent of tropical floodplains was prescribed using output from the spatially explicit hydrology model PCR-GLOBWB. We introduced new plant functional types (PFTs) that explicitly represent floodplain vegetation. The PFT parameterizations were evaluated against available remote-sensing data sets (GLC2000 land cover and MODIS Net Primary Productivity). Simulated CH₄ flux densities were evaluated against field observations and regional flux inventories. Simulated CH₄ emissions at Amazon Basin scale were compared to model simulations performed in the WETCHIMP intercomparison project. We found that LPX reproduces the average magnitude of observed net CH₄ flux densities for the Amazon Basin. However, the model does not reproduce the variability between sites or between years within a site. Unfortunately, site information is too limited to attest or disprove some model features. At the Amazon Basin scale, our results underline the large uncertainty in the magnitude of wetland CH₄ emissions. Sensitivity analyses gave insights into the main drivers of floodplain CH₄ emission and their associated uncertainties. In particular, uncertainties in floodplain extent (i.e., difference between GLC2000 and PCR-GLOBWB output) modulate the simulated emissions by a factor of about 2. Our best estimates, using PCR-GLOBWB in combination with GLC2000, lead to simulated Amazon-integrated emissions of 44.4 ± 4.8 Tg yr⁻¹. Additionally, the LPX emissions are highly sensitive to vegetation distribution. Two simulations with the same mean PFT cover, but different spatial distributions of grasslands within the basin, modulated emissions by about 20%. Correcting the LPX-simulated NPP using MODIS reduces the Amazon emissions by 11.3%. Finally, due to an intrinsic limitation of LPX to account for seasonality in floodplain extent, the model failed to reproduce the full dynamics in CH₄ emissions but we proposed solutions to this issue. The interannual variability (IAV) of the emissions increases by 90% if the IAV in floodplain extent is accounted for, but still remains lower than in most of the WETCHIMP models. While our model includes more mechanisms specific to tropical floodplains, we were unable to reduce the uncertainty in the magnitude of wetland CH₄ emissions of the Amazon Basin. Our results helped identify and prioritize directions towards more accurate estimates of tropical CH₄ emissions, and they stress the need for more research to constrain floodplain CH₄ emissions and their temporal variability, even before including other fundamental mechanisms such as floating macrophytes or lateral water fluxes

Angular 21 cm Power Spectrum of a Scaling Distribution of Cosmic String Wakes

Cosmic string wakes lead to a large signal in 21 cm redshift maps at redshifts larger than that corresponding to reionization. Here, we compute the angular power spectrum of 21 cm radiation as predicted by a scaling distribution of cosmic strings whose wakes have undergone shock heating.Comment: 13 pages, 6 figures; v2: minor modifications, journal versio

Equation of state of cosmic strings with fermionic current-carriers

The relevant characteristic features, including energy per unit length and tension, of a cosmic string carrying massless fermionic currents in the framework of the Witten model in the neutral limit are derived through quantization of the spinor fields along the string. The construction of a Fock space is performed by means of a separation between longitudinal modes and the so-called transverse zero energy solutions of the Dirac equation in the vortex. As a result, quantization leads to a set of naturally defined state parameters which are the number densities of particles and anti-particles trapped in the cosmic string. It is seen that the usual one-parameter formalism for describing the macroscopic dynamics of current-carrying vortices is not sufficient in the case of fermionic carriers.Comment: 30 pages, 15 figures, uses ReVTeX, equation of state corrected, comments and references added. Accepted for publication in Phys. Rev.

The 21 cm Signature of Shock Heated and Diffuse Cosmic String Wakes

The analysis of the 21 cm signature of cosmic string wakes is extended in several ways. First we consider the constraints on $G\mu$ from the absorption signal of shock heated wakes laid down much later than matter radiation equality. Secondly we analyze the signal of diffuse wake, that is those wakes in which there is a baryon overdensity but which have not shock heated. Finally we compare the size of these signals compared to the expected thermal noise per pixel which dominates over the background cosmic gas brightness temperature and find that the cosmic string signal will exceed the thermal noise of an individual pixel in the Square Kilometre Array for string tensions $G\mu > 2.5 \times 10^{-8}$.Comment: 10 pages, 4 figures, Appendix added, version published in JCA