Articulatory features for speech-driven head motion synthesis

This study investigates the use of articulatory features for speech-driven head motion synthesis as opposed to prosody features such as F0 and energy that have been mainly used in the literature. In the proposed approach, multi-stream HMMs are trained jointly on the synchronous streams of speech and head motion data. Articulatory features can be regarded as an intermediate parametrisation of speech that are expected to have a close link with head movement. Measured head and articulatory movements acquired by EMA were synchronously recorded with speech. Measured articulatory data was compared to those predicted from speech using an HMM-based inversion mapping system trained in a semi-supervised fashion. Canonical correlation analysis (CCA) on a data set of free speech of 12 people shows that the articulatory features are more correlated with head rotation than prosodic and/or cepstral speech features. It is also shown that the synthesised head motion using articulatory features gave higher correlations with the original head motion than when only prosodic features are used. Index Terms: head motion synthesis, articulatory features, canonical correlation analysis, acoustic-to-articulatory mappin

Modelling heterogeneity in response behaviour towards a sequence of discrete choice questions: a latent class approach

There is a growing body of evidence in the non-market valuation literature suggesting that responses to a sequence of discrete choice questions tend to violate the assumptions typically made by analysts regarding independence of responses and stability of preferences. Heuristics such as value learning and strategic misrepresentation have been offered as explanations for these results. While a few studies have tested these heuristics as competing hypotheses, none have investigated the possibility that each explains the response behaviour of a subgroup of the population. In this paper, we make a contribution towards addressing this research gap by presenting an equality-constrained latent class model designed to estimate the proportion of respondents employing each of the proposed heuristics. We demonstrate the model on binary and multinomial choice data sources and find three distinct types of response behaviour. The results suggest that accounting for heterogeneity in response behaviour may be a better way forward than attempting to identify a single heuristic to explain the behaviour of all respondents

The University of Edinburgh Head-Motion and Audio Storytelling (UoE-HAS) Dataset

Abstract. In this paper we announce the release of a large dataset of storytelling monologue with motion capture for the head and body. Initial tests on the dataset indicate that head motion is more dependant on the speaker than the style of speech

Central limit theorems for the spectra of classes of random fractals

We discuss the spectral asymptotics of some open subsets of the real line with random fractal boundary and of a random fractal, the continuum random tree. In the case of open subsets with random fractal boundary we establish the existence of the second order term in the asymptotics almost surely and then determine when there will be a central limit theorem which captures the fluctuations around this limit. We will show examples from a class of random fractals generated from Dirichlet distributions as this is a relatively simple setting in which there are sets where there will and will not be a central limit theorem. The Brownian continuum random tree can also be viewed as a random fractal generated by a Dirichlet distribution. The first order term in the spectral asymptotics is known almost surely and here we show that there is a central limit theorem describing the fluctuations about this, though the positivity of the variance arising in the central limit theorem is left open. In both cases these fractals can be described through a general Crump-Mode-Jagers branching process and we exploit this connection to establish our central limit theorems for the higher order terms in the spectral asymptotics. Our main tool is a central limit theorem for such general branching processes which we prove under conditions which are weaker than those previously known

Isotropic magnetometry with simultaneous excitation of orientation and alignment CPT resonances

Atomic magnetometers have very high absolute precision and sensitivity to magnetic fields but suffer from a fundamental problem: the vectorial or tensorial interaction of light with atoms leads to "dead zones", certain orientations of magnetic field where the magnetometer loses its sensitivity. We demonstrate a simple polarization modulation scheme that simultaneously creates coherent population trapping (CPT) in orientation and alignment, thereby eliminating dead zones. Using $^{87}$Rb in a 10 Torr buffer gas cell we measure narrow, high-contrast CPT transparency peaks in all orientations and also show absence of systematic effects associated with non-linear Zeeman splitting.Comment: 4 pages, 4 figure

Households’ Willingness to Pay for Undergrounding Electricity and Telecommunications Wires

Underground telecommunications and low-voltage electricity networks have several advantages over overhead networks including reliability of supply, safety and improved visual amenity. The economic viability of replacing existing overhead networks with new underground networks depends on the value of these benefits to households, but no complete value estimates are available in the literature. This paper represents a contribution towards addressing this research gap. A stated choice survey is used to estimate willingness-to-pay for undergrounding in established residential areas in Canberra. Average willingness-to-pay is at least $6,838 per household and there is significant variation in preferences over the population. The results suggest that benefits would be highest in areas with higher household income and older residents where visual amenity, safety, tree trimming or restrictions on the use of yard space are of concern.Stated preference; willingness-to-pay; undergrounding; supply reliability

Modelling heterogeneity in response behaviour towards a sequence of discrete choice questions: a latent class approach

There is a growing body of evidence in the non-market valuation literature suggesting that responses to a sequence of discrete choice questions tend to violate the assumptions typically made by analysts regarding independence of responses and stability of preferences. Heuristics such as value learning and strategic misrepresentation have been offered as explanations for these results. While a few studies have tested these heuristics as competing hypotheses, none have investigated the possibility that each explains the response behaviour of a subgroup of the population. In this paper, we make a contribution towards addressing this research gap by presenting an equality-constrained latent class model designed to estimate the proportion of respondents employing each of the proposed heuristics. We demonstrate the model on binary and multinomial choice data sources and find three distinct types of response behaviour. The results suggest that accounting for heterogeneity in response behaviour may be a better way forward than attempting to identify a single heuristic to explain the behaviour of all respondents.Choice experiment; latent class; ordering effects; strategic response; willingness-to-pay

A comparison of responses to single and repeated discrete choice questions

According to neoclassical economic theory, a stated preference elicitation format comprising a single binary choice between the status quo and one alternative is incentive compatible under certain conditions. Formats typically used in choice experiments comprising a sequence of discrete choice questions do not hold this property. In this paper, the effect on stated preferences of expanding the number of binary choice tasks per respondent from one to four is tested using a split sample treatment in an attribute-based survey relating to the undergrounding of overhead electricity and telecommunications wires. We find evidence to suggest that presenting multiple choice tasks per respondent decreases estimates of expected willingness to pay. Preferences stated in the first of a sequence of choice tasks are not significantly different from those stated in the incentive compatible single binary choice task, but, in subsequent choice tasks, responses are influenced by cost levels observed in past questions. Three behavioural explanations can be advanced – weak strategic misrepresentation, reference point revision and cost-driven value learning. The evidence is contrary to the standard assumption of truthful response with stable preferences.Choice experiment; willingness-to-pay; incentive compatibility; order effects; undergrounding

Convolutional Analysis Operator Learning: Dependence on Training Data

Convolutional analysis operator learning (CAOL) enables the unsupervised training of (hierarchical) convolutional sparsifying operators or autoencoders from large datasets. One can use many training images for CAOL, but a precise understanding of the impact of doing so has remained an open question. This paper presents a series of results that lend insight into the impact of dataset size on the filter update in CAOL. The first result is a general deterministic bound on errors in the estimated filters, and is followed by a bound on the expected errors as the number of training samples increases. The second result provides a high probability analogue. The bounds depend on properties of the training data, and we investigate their empirical values with real data. Taken together, these results provide evidence for the potential benefit of using more training data in CAOL.Comment: 5 pages, 2 figure