Bayesian networks for predicting duration of phones

In a concatenative text-to-speech (TTS) system, the duration of a phonetic segment (phone) is predicted by a duration model which is usually trained using a database of feature vectors, that consist of a set of linguistic factors' (attributes') values describing a phone in a particular context. In general, databases used to train phone duration models are unbalanced. However, it has been shown that the probability of a rare feature vector occurring even in a small sample of text is quite high. Furthermore, factors affecting phone's duration interact; a set of two or more factors may amplify or attenuate the affect of other factors. A robust model for predicting phone duration must generalise well in order to successfully predict the durations of phones with these rare feature vectors. Since linguistic factors affecting segment duration interact, we would expect that modelling these factor interactions will give a better model. There have been a number of models developed for predicting a phone's duration, ranging from rule-based to neural nets to classification and regression tree (CART) to sums-of-products (SoP) mod¬ els. In the CART model, a phone's duration is predicted by a decision tree. The tree is built by recursively clustering the training data into subsets that share common values for certain attributes of the feature vectors. The duration of a phone is then predicted by using the tree to find the data cluster that matches as many of the feature vector attributes as possible. The CART model is easy to build, robust to errors in data but performs poorly when the percent of missing data is too high. In the SoP model, the log of a phone's duration is predicted as a sum of factors' product terms. The SoP model predicts phone duration with high accuracy, even in cases of hidden or missing data. However, this is done at the cost of substantial data pre-processing. In addition, the number of different sums-of-products models grows hyper-exponentially with the number of factors. Therefore, one must use some heuristic search techniques to find the model that fits the data the best. In our work, we use a Bayesian belief network (BN) consisting of discrete nodes for the linguistic factors and a single continuous node for the phone's duration. Interactions between factors are represented as conditional dependency relations in this graphical model. During train¬ ing, the parameters of the belief network are learned via the Expectation Maximisation (EM) algorithm. The duration of each phone in the test set is then predicted via Bayesian inference: given the parameters of the be¬ lief network, we calculate the probability of a phone taking on a particular duration given the observations of the linguistic variables. The duration value with the maximum probability is chosen as the phone's duration. We contrasted the results of the belief network model with those of the sums of products and CART models. We trained and tested all three models on the same data. In terms of the RMS error our BN model performs better than both CART and SoP models. In terms of the correlation coefficient, our BN model performs better than SoP model, and no worse than CART model. We believe our Bayesian model has many advantages compared to CART and SoP models. For instance, it captures the factors' interactions in a concise way by causal relationships among the variables in the graphical model. The Bayesian model also makes robust predictions of phone duration in cases of missing or hidden data

Frontogenesis of the Angola-Benguela Frontal Zone

A diagnostic analysis of the climatological annual mean and seasonal cycle of the Angola–Benguela Frontal Zone (ABFZ) is performed by applying an ocean frontogenetic function (OFGF) to the ocean mixing layer (OML). The OFGF reveals that the meridional confluence and vertical tilting terms are the most dominant contributors to the frontogenesis of the ABFZ. The ABFZ shows a well-pronounced semiannual cycle with two maximum (minimum) peaks in April–May and November–December (February–March and July–August). The development of the two maxima of frontogenesis is due to two different physical processes: enhanced tilting from March to April and meridional confluence from September to October. The strong meridional confluence in September to October is closely related to the seasonal southward intrusion of tropical warm water to the ABFZ that seems to be associated with the development of the Angola Dome northwest of the ABFZ. The strong tilting effect from March to April is attributed to the meridional gradient of vertical velocities, whose effect is amplified in this period due to increasing stratification and shallow OML depth. The proposed OFGF can be viewed as a tool to diagnose the performance of coupled general circulation models (CGCMs) that generally fail at realistically simulating the position of the ABFZ, which leading to huge warm biases in the southeastern Atlantic.publishedVersio

Frontogenesis of the Angola–Benguela Frontal Zone

A diagnostic analysis of the climatological annual mean and seasonal cycle of the Angola–Benguela Frontal Zone (ABFZ) is performed by applying an ocean frontogenetic function (OFGF) to the ocean mixing layer (OML). The OFGF reveals that the meridional confluence and vertical tilting terms are the most dominant contributors to the frontogenesis of the ABFZ. The ABFZ shows a well-pronounced semiannual cycle with two maximum (minimum) peaks in April–May and November–December (February–March and July–August). The development of the two maxima of frontogenesis is due to two different physical processes: enhanced tilting from March to April and meridional confluence from September to October. The strong meridional confluence in September to October is closely related to the seasonal southward intrusion of tropical warm water to the ABFZ that seems to be associated with the development of the Angola Dome northwest of the ABFZ. The strong tilting effect from March to April is attributed to the meridional gradient of vertical velocities, whose effect is amplified in this period due to increasing stratification and shallow OML depth. The proposed OFGF can be viewed as a tool to diagnose the performance of coupled general circulation models (CGCMs) that generally fail at realistically simulating the position of the ABFZ, which leading to huge warm biases in the southeastern Atlantic.</p

Bayesian modelling of vowel segment duration for text-to-speech synthesis using distinctive features

We report the results of applying the Bayesian Belief Network (BN) approach to predicting vowel duration. A Bayesian inference of the vowel duration is performed on a hybrid Bayesian network consisting of discrete and continuous nodes, with the nodes in the network representing the linguistic factors that affect segment duration. New to the present research, we model segment identity factor as a set of distinctive features. The features chosen were height, frontness, length, and roundness. We also experimented with a word class feature that implicitly represents word frequency information. We contrasted the results of the belief network model with those of the sums of products (SoP) model and classification and regression tree (CART) model. We trained and tested all three models on the same data. In terms of the RMS error and correlation coefficient, our BN model performs no worse than SoP model, and it significantly outperforms CART model

Role of wind stress in driving SST biases in the tropical Atlantic

Coupled climate models used for long-term future climate projections and seasonal or decadal predictions share a systematic and persistent warm sea surface temperature (SST) bias in the tropical Atlantic. This study attempts to better understand the physical mechanisms responsible for the development of systematic biases in the tropical Atlantic using the so-called Transpose-CMIP protocol in a multi-model context. Six global climate models have been used to perform seasonal forecasts starting both in May and February over the period 2000-2009. In all models, the growth of SST biases is rapid. Significant biases are seen in the first month of forecast and, by six months, the root-mean-square SST bias is 80% of the climatological bias. These control experiments show that the equatorial warm SST bias is not driven by surface heat flux biases in all models, whereas in the south-eastern Atlantic the solar heat flux could explain the setup of an initial warm bias in the first few days. A set of sensitivity experiments with prescribed wind stress confirm the leading role of wind stress biases in driving the equatorial SST bias, even if the amplitude of the SST bias is model dependent. A reduced SST bias leads to a reduced precipitation bias locally, but there is no robust remote effect on West African Monsoon rainfall. Over the south-eastern part of the basin, local wind biases tend to have an impact on the local SST bias (except in the high resolution model). However, there is also a non-local effect of equatorial wind correction in two models. This can be explained by sub-surface advection of water from the equator, which is colder when the bias in equatorial wind stress is corrected. In terms of variability, it is also shown that improving the mean state in the equatorial Atlantic leads to a beneficial intensification of the Bjerknes feedback loop. In conclusion, we show a robust effect of wind stress biases on tropical mean climate and variability in multiple climate models

Observed and Projected Hydroclimate Changes in the Andes

The Andes is the most biodiverse region across the globe. In addition, some of the largest urban areas in South America are located within this region. Therefore, ecosystems and human population are affected by hydroclimate changes reported at global, regional and local scales. This paper summarizes progress of knowledge about long-term trends observed during the last two millennia over the entire Andes, with more detail for the period since the second half of the 20th century, and presents a synthesis of climate change projections by the end of the 21st century. In particular, this paper focuses on temperature, precipitation and surface runoff in the Andes. Changes in the Andean cryosphere are not included here since this particular topic is discussed in other paper in this Frontiers special issue, and elsewhere (e.g. IPCC,2019b). While previous works have reviewed the hydroclimate of South America and particular sectors (i.e., Amazon and La Plata basins, the Altiplano, Northern South America, etc.) this review includes for the first time the entire Andes region, considering all latitudinal ranges: tropical (North of 27°S), subtropical (27°S−37°S) and extratropical (South of 37°S). This paper provides a comprehensive view of past and recent changes, as well as available climate change projections, over the entire Andean range. From this review, the main knowledge gaps are highlighted and urgent research necessities in order to provide more mechanistic understanding of hydroclimate changes in the Andes and more confident projections of its possible changes in association with global climate change.Fil: Pabón Caicedo, José Daniel. Universidad Nacional de Colombia; ColombiaFil: Arias, Paola A.. Universidad de Antioquia; ColombiaFil: Carril, Andrea Fabiana. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; ArgentinaFil: Espinoza, Jhan Carlo. Universite Grenoble Alpes; FranciaFil: Fita Borrell, Lluís. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; ArgentinaFil: Goubanova, Katerina. Centro de Estudios Avanzados en Zonas Áridas; ChileFil: Lavado Casimiro, Waldo. No especifíca;Fil: Masiokas, Mariano Hugo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Provincia de Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Universidad Nacional de Cuyo. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales; ArgentinaFil: Solman, Silvina Alicia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; ArgentinaFil: Villalba, Ricardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Provincia de Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Universidad Nacional de Cuyo. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales; Argentin

Impact of climate and land cover changes on snow cover in a small Pyrenean catchment

International audienceThe seasonal snow in the Pyrenees Mountains is an essential source of runoff for hydropower production and crop irrigation in Spain and France. The Pyrenees are expected to undergo strong environmental perturbations over the 21st century because of climate change (rising temperatures) and the abandonment of agro-pastoral areas (reforestation). Both changes are happening at similar timescales and are expected to have an impact on snow cover. The effect of climate change on snow in the Pyrenees is well understood , but the effect of land cover changes is much less documented. Here, we analyze the response of snow cover to a combination of climate and land cover change scenarios in a small Pyrenean catchment (Bassiès, 14.5 km 2 , elevation range 940–2651 m a.s.l.) using a distributed snowpack evolution model. Climate scenarios were constructed from the output of regional climate model projections, whereas land cover scenarios were generated based on past observed changes and an inductive pattern-based model. The model was validated over a snow season using in situ snow depth measurements and high-resolution snow cover maps derived from SPOT (Satellite Pour l'Observation de la Terre – Earth Observation Satellite) satellite images. Model projections indicate that both climate and land cover changes reduce the mean snow depth. However, the impact on the snow cover duration is moderated in reforested areas by the shading effect of trees on the snow surface radiation balance. Most of the significant changes are expected to occur in the transition zone between 1500 m a.s.l. and 2000 m a.s.l. where (i) the projected increase in air temperatures decreases the snow fraction of the precipitation and (ii) the land cover changes are concentrated. However, the consequences on the runoff are limited because most of the meltwater originates from high-elevation areas of the catchment, which are less affected by climate change and reforestation