Perceptual learning of liquids

Previous research on lexically-guided perceptual learning has focussed on contrasts that differ primarily in local cues, such as plosive and fricative contrasts. The present research had two aims: to investigate whether perceptual learning occurs for a contrast with non-local cues, the /l/-/r/ contrast, and to establish whether STRAIGHT can be used to create ambiguous sounds on an /l/-/r/ continuum. Listening experiments showed lexically-guided learning about the /l/-/r/ contrast. Listeners can thus tune in to unusual speech sounds characterised by non-local cues. Moreover, STRAIGHT can be used to create stimuli for perceptual learning experiments, opening up new research possibilities. Index Terms: perceptual learning, morphing, liquids, human word recognition, STRAIGHT.The research by Odette Scharenborg was partly sponsored by the Max Planck International Research Network on Aging. We thank Denise Moerel, Laurence Bruggeman, Lies Cuijpers, Michael Wiechers, Willemijn van den Berg, and Zhou Fang for assistance in preparing and running these experiments and Marijt Witteman for recording the stimuli.peer-reviewe

Perceptual learning of liquids in older listeners

Numerous studies have shown that young listeners can adapt to idiosyncratic pronunciations through lexically-guided perceptual learning (McQueen et al., 2006; Norris et al., 2003). Aging may affect sensitivity to the higher frequencies in the speech signal, which results in the loss of sensitivity to phonetic detail. Nevertheless, short-term adaptation to accents and to time-compressed speech seems to be preserved with aging and with hearing loss (Adank & Janse, 2010; Gordon-Salant et al., 2010). However, the extent of the flexibility of phoneme categories and the conditions under which these phoneme boundary shifts can or cannot occur in an older population have not been investigated yet. This research investigates whether older listeners are able to tune into a speaker like young normal-hearing listeners can, by comparing the perceptual learning effect of older listeners (aged 60+, varying in their hearing sensitivity) and young (normal-hearing) listeners. Moreover, we investigate whether hearing loss affects the ability to learn non-standard phoneme pronunciations. Hearing loss may interfere with perceptual learning, as perceptual evidence in favour of a certain pronunciation variant is weaker. We therefore expected the perceptual learning effect of older listeners to be weaker and less stable than for young listeners. 36 young and 60 older listeners were exposed to an ambiguous [l/ɹ] in Dutch words ending in either /r/ or /l/ and to Dutch words ending in natural /r/ and /l/, in a lexical decision task (following Norris et al., 2003; Scharenborg et al., 2011). Young listeners gave significantly more correct answers to natural than to ambiguous stimuli (p<0.001). Older listeners had fewer correct answers to the natural stimuli (p<0.05), but showed relatively less impact of stimulus ambiguity (p<0.005). Young listeners gave significantly slower responses to ambiguous than to natural stimuli (p<0.001). Older listeners gave slower responses to the natural stimuli than the young listeners (p<0.05), but again were less impacted by stimulus ambiguity (p<0.005). In a subsequent phonetic categorisation task, listeners were confronted with a range of ambiguous sounds from the [l]-[ɹ]-continuum. The results revealed that listeners exposed to ambiguous [l/ɹ] in /r/-final words gave significantly more /r/-responses than listeners exposed to [l/ɹ] in /l/-final words (p<0.001; see also Figure 1). This effect was significantly stronger for the young listeners in block 1, but not so in the subsequent blocks. After dividing the older listener group into one better-hearing and one poorer-hearing group, no interaction was found between exposure condition and hearing status, suggesting that the age group difference in the size of the initial learning effect may not be due to hearing status. Contrary to our expectations, the learning effect for the older listeners remained stable over blocks, while the young listeners showed ‘unlearning’; i.e., the difference in percentage /r/-responses between the two exposure groups of young listeners grew significantly smaller over blocks. Concluding, the learning effect is stronger right after exposure for young listeners, while the effect is longer lasting for older listeners. Our results show that older listeners, with and without hearing loss, can still retune their phoneme categories to facilitate word recognition. Hearing loss does not seem to interfere with perceptual learning. Our results are in line with other evidence that the perceptual system remains flexible throughout the lifespan (Adank & Janse, 2010; Golomb et al., 2007; Peelle & Wingfield, 2005)

The audiovisual structure of onomatopoeias: An intrusion of real-world physics in lexical creation

Sound-symbolic word classes are found in different cultures and languages worldwide. These words are continuously produced to code complex information about events. Here we explore the capacity of creative language to transport complex multisensory information in a controlled experiment, where our participants improvised onomatopoeias from noisy moving objects in audio, visual and audiovisual formats. We found that consonants communicate movement types (slide, hit or ring) mainly through the manner of articulation in the vocal tract. Vowels communicate shapes in visual stimuli (spiky or rounded) and sound frequencies in auditory stimuli through the configuration of the lips and tongue. A machine learning model was trained to classify movement types and used to validate generalizations of our results across formats. We implemented the classifier with a list of cross-linguistic onomatopoeias simple actions were correctly classified, while different aspects were selected to build onomatopoeias of complex actions. These results show how the different aspects of complex sensory information are coded and how they interact in the creation of novel onomatopoeias.Fil: Taitz, Alan. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaFil: Assaneo, María Florencia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaFil: Elisei, Natalia Gabriela. Universidad de Buenos Aires. Facultad de Medicina; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Tripodi, Monica Noemi. Universidad de Buenos Aires; ArgentinaFil: Cohen, Laurent. Centre National de la Recherche Scientifique; Francia. Universite Pierre et Marie Curie; Francia. Institut National de la Santé et de la Recherche Médicale; FranciaFil: Sitt, Jacobo Diego. Centre National de la Recherche Scientifique; Francia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Institut National de la Santé et de la Recherche Médicale; Francia. Universite Pierre et Marie Curie; FranciaFil: Trevisan, Marcos Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentin

The diachronic emergence of retroflex segments in three languages

The present study shows that though retroflex segments can be considered articulatorily marked, there are perceptual reasons why languages introduce this class into their phoneme inventory. This observation is illustrated with the diachronic developments of retroflexes in Norwegian (North- Germanic), Nyawaygi (Australian) and Minto-Nenana (Athapaskan). The developments in these three languages are modelled in a perceptually oriented phonological theory, since traditional articulatorily-based features cannot deal with such processes

Deep Fluids: A Generative Network for Parameterized Fluid Simulations

This paper presents a novel generative model to synthesize fluid simulations from a set of reduced parameters. A convolutional neural network is trained on a collection of discrete, parameterizable fluid simulation velocity fields. Due to the capability of deep learning architectures to learn representative features of the data, our generative model is able to accurately approximate the training data set, while providing plausible interpolated in-betweens. The proposed generative model is optimized for fluids by a novel loss function that guarantees divergence-free velocity fields at all times. In addition, we demonstrate that we can handle complex parameterizations in reduced spaces, and advance simulations in time by integrating in the latent space with a second network. Our method models a wide variety of fluid behaviors, thus enabling applications such as fast construction of simulations, interpolation of fluids with different parameters, time re-sampling, latent space simulations, and compression of fluid simulation data. Reconstructed velocity fields are generated up to 700x faster than re-simulating the data with the underlying CPU solver, while achieving compression rates of up to 1300x.Comment: Computer Graphics Forum (Proceedings of EUROGRAPHICS 2019), additional materials: http://www.byungsoo.me/project/deep-fluids

Training novel phonemic contrasts: a comparison of identification and oddity discrimination training

High Variability Pronunciation Training (HVPT) is a highly successful alternative to ASR-based pronunciation training. It has been demonstrated that HVPT is effective in teaching the perception of non-native phonemic contrasts, and that this skill generalizes to the perception of unfamiliar words and talkers, transfers to pronunciation, and is retained long-term. HVPT is, however, not efficient and hence not motivating for the learner. In this study, we therefore compare HVPT with an alternative, namely oddity discrimination training. This comparison, in which Mandarin-Chinese speakers were trained to pronounce the English /r/-/l/ phonemic contrast, provides preliminary evidence to support the use of discrimination tasks in addition to identification tasks to add variety to HVPT

Modeling the emergence of universality in color naming patterns

The empirical evidence that human color categorization exhibits some universal patterns beyond superficial discrepancies across different cultures is a major breakthrough in cognitive science. As observed in the World Color Survey (WCS), indeed, any two groups of individuals develop quite different categorization patterns, but some universal properties can be identified by a statistical analysis over a large number of populations. Here, we reproduce the WCS in a numerical model in which different populations develop independently their own categorization systems by playing elementary language games. We find that a simple perceptual constraint shared by all humans, namely the human Just Noticeable Difference (JND), is sufficient to trigger the emergence of universal patterns that unconstrained cultural interaction fails to produce. We test the results of our experiment against real data by performing the same statistical analysis proposed to quantify the universal tendencies shown in the WCS [Kay P and Regier T. (2003) Proc. Natl. Acad. Sci. USA 100: 9085-9089], and obtain an excellent quantitative agreement. This work confirms that synthetic modeling has nowadays reached the maturity to contribute significantly to the ongoing debate in cognitive science.Comment: Supplementery Information available here http://www.pnas.org/content/107/6/2403/suppl/DCSupplementa

Visual Closed-Loop Control for Pouring Liquids

Pouring a specific amount of liquid is a challenging task. In this paper we develop methods for robots to use visual feedback to perform closed-loop control for pouring liquids. We propose both a model-based and a model-free method utilizing deep learning for estimating the volume of liquid in a container. Our results show that the model-free method is better able to estimate the volume. We combine this with a simple PID controller to pour specific amounts of liquid, and show that the robot is able to achieve an average 38ml deviation from the target amount. To our knowledge, this is the first use of raw visual feedback to pour liquids in robotics.Comment: To appear at ICRA 201