1,720,985 research outputs found
A new Italian dataset of parallel acoustic and articulatory data
No full textIn this paper we introduce a new Italian dataset consisting of simultaneous recordings of continuous speech and trajectories of important vocal tract articulators (i.e. tongue, lips, incisors) tracked by Electromagnetic Articulography (EMA). It includes more than 500 sentences uttered in citation condition by three speakers, one male (cnz) and two females (lls, olm), for approximately 2 hours of speech material. Such dataset has been designed to be large enough and phonetically balanced so as to be used in speech applications (e.g. speech recognition systems). We then test our speaker-dependent articulatory Deep- Neural-Network Hidden-Markov-Model (DNN-HMM) phone recognizer on the set of data recorded from the cnz speaker. We show that phone recognition results are comparable to the ones that we previously obtained using two well-known British-English datasets with EMA data of equivalent vocal tract articulators. That suggests that the new set of data is a equally useful and coherent resource. The dataset is the session 1 of a larger Italian corpus, called Multi-SPeaKing-style-Articulatory (MSPKA) corpus, including parallel audio and articulatory data in diverse speaking styles (e.g. read, hyperarticulated and hypoarticulated speech). It is freely available at http://www.mspkacorpus.it for research purposes. In the immediate future the whole corpus will be released
Integrating articulatory data in deep neural network-based acoustic modeling
No full textHybrid deep neural network-hidden Markov model (DNN-HMM) systems have become the state-of-the-art in automatic speech recognition. In this paper we experiment with DNN-HMM phone recognition systems that use measured articulatory information. Deep neural networks are both used to compute phone posterior probabilities and to perform acoustic-to-articulatory mapping (AAM). The AAM processes we propose are based on deep representations of the acoustic and the articulatory domains. Such representations allow to: (i) create different pre-training configurations of the DNNs that perform AAM; (ii) perform AAM on a transformed (through DNN autoencoders) articulatory feature (AF) space that captures strong statistical dependencies between articulators. Traditionally, neural networks that approximate the AAM are used to generate AFs that are appended to the observation vector of the speech recognition system. Here we also study a novel approach (AAM-based pretraining) where a DNN performing the AAM is instead used to pretrain the DNN that computes the phone posteriors. Evaluations on both the MOCHA-TIMIT msak0 and the mngu0 datasets show that: (i) the recovered AFs reduce phone error rate (PER) in both clean and noisy speech conditions, with a maximum 10.1% relative phone error reduction in clean speech conditions obtained when autoencoder-transformed AFs are used; (ii) AAM-based pretraining could be a viable strategy to exploit the available small articulatory datasets to improve acoustic models trained on large acoustic-only datasets. © 2015 Elsevier Ltd
Relevance-weighted-reconstruction of articulatory features in deep-neural-network-based acoustic-to-articulatory mapping
No full textWe present a strategy for learning Deep-Neural-Network (DNN)-based Acoustic-to-Articulatory Mapping (AAM) functions where the contribution of an articulatory feature (AF) to the global reconstruction error is weighted by its relevance. We first empirically show that when an articulator is more crucial for the production of a given phone it is less variable, confirming previous findings. We then compute the relevance of an articulatory feature as a function of its frame-wise variance dependent on the acoustic evidence which is estimated through a Mixture Density Network (MDN). Finally we combine acoustic and recovered articulatory features in a hybrid DNN-HMM phone recognizer. Tested on the MOCHA-TIMIT corpus, articulatory features reconstructed by a standardly trained DNN lead to a 8.4% relative phone error reduction (w.r.t. a recognizer that only uses MFCCs), whereas when the articulatory features are reconstructed taking into account their relevance the relative phone error reduction increased to 10.9
Riscoperta di materiali e tecnologie antiche in liuteria: analisi diagnostiche sui frammenti della viola da gamba Joannes Marcus (II metà del XVI secolo).
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Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Deep-level acoustic-to-articulatory mapping for DBN-HMM based phone recognition
No full textIn this paper we experiment with methods based on Deep Belief Networks (DBNs) to recover measured articulatory data from speech acoustics. Our acoustic-to-articulatory mapping (AAM) processes go through multi-layered and hierarchical (i.e., deep) representations of the acoustic and the articulatory domains obtained through unsupervised learning of DBNs. The unsupervised learning of DBNs can serve two purposes: (i) pre-training of the Multi-layer Perceptrons that perform AAM; (ii) transformation of the articulatory domain that is recovered from acoustics through AAM. The recovered articulatory features are combined with MFCCs to compute phone posteriors for phone recognition. Tested on the MOCHA-TIMIT corpus, the recovered articulatory features, when combined with MFCCs, lead to up to a remarkable 16.6% relative phone error reduction w.r.t. a phone recognizer that only use
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