33,689 research outputs found

    Synthetic speech detection using phase information

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    Taking advantage of the fact that most of the speech processing techniques neglect the phase information, we seek to detect phase perturbations in order to prevent synthetic impostors attacking Speaker Verification systems. Two Synthetic Speech Detection (SSD) systems that use spectral phase related information are reviewed and evaluated in this work: one based on the Modified Group Delay (MGD), and the other based on the Relative Phase Shift, (RPS). A classical module-based MFCC system is also used as baseline. Different training strategies are proposed and evaluated using both real spoofing samples and copy-synthesized signals from the natural ones, aiming to alleviate the issue of getting real data to train the systems. The recently published ASVSpoof2015 database is used for training and evaluation. Performance with completely unrelated data is also checked using synthetic speech from the Blizzard Challenge as evaluation material. The results prove that phase information can be successfully used for the SSD task even with unknown attacks.This work has been partially supported by the Basque Government (ElkarOla Project, KK-2015/00,098) and the Spanish Ministry of Economy and Competitiveness (Restore project, TEC2015-67,163-C2-1-R)

    Syntactic processing as a marker for cognitive impairment in amyotrophic lateral sclerosis

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    Despite recent interest in cognitive changes in patients with amyotrophic lateral sclerosis (ALS), investigations of language function looking at the level of word, sentence and discourse processing are relatively scarce. Data were obtained from 26 patients with sporadic ALS and 26 healthy controls matched for age, education, gender, anxiety, depression and executive function performance. Standardized language tasks included confrontation naming, semantic access, and syntactic comprehension. Quantitative production analysis (QPA) was used to analyse connected speech samples of the Cookie Theft picture description task. Results showed that the ALS patients were impaired on standardized measures of grammatical comprehension and action/verb semantics. At the level of discourse, ALS patients were impaired on measures of syntactic complexity and fluency; however, the latter could be better explained by disease related factors. Discriminant analysis revealed that syntactic measures differentiated ALS patients from controls. In conclusion, patients with ALS exhibit deficits in receptive and expressive language on tasks of comprehension and connected speech production, respectively. Our findings suggest that syntactic processing deficits seem to be the predominant feature of language impairment in ALS and that these deficits can be detected by relatively simple language tests

    Unsupervised discovery of temporal sequences in high-dimensional datasets, with applications to neuroscience.

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    Identifying low-dimensional features that describe large-scale neural recordings is a major challenge in neuroscience. Repeated temporal patterns (sequences) are thought to be a salient feature of neural dynamics, but are not succinctly captured by traditional dimensionality reduction techniques. Here, we describe a software toolbox-called seqNMF-with new methods for extracting informative, non-redundant, sequences from high-dimensional neural data, testing the significance of these extracted patterns, and assessing the prevalence of sequential structure in data. We test these methods on simulated data under multiple noise conditions, and on several real neural and behavioral datas. In hippocampal data, seqNMF identifies neural sequences that match those calculated manually by reference to behavioral events. In songbird data, seqNMF discovers neural sequences in untutored birds that lack stereotyped songs. Thus, by identifying temporal structure directly from neural data, seqNMF enables dissection of complex neural circuits without relying on temporal references from stimuli or behavioral outputs

    Impact of chronic somatoform and osteoarthritis pain on conscious and preconscious cognitive processing

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    The study investigates the impact of chronic pain (CP) on conscious and preconscious cognitive processes and on guessing behavior, and examines the mediating effect of a depressive state. Twenty-eight patients with CP due to hip osteoarthritis, 32 patients with a somatoform disorder including pain symptoms, and 31 participants who did not have CP were examined within the framework of a modified Process-Dissociation-Procedure. Neutral, health threatening and general threatening stimuli were presented acoustically in a lexical decision task. Parameters of conscious processing, preconscious processing, and of chance were estimated by a multinomial modelling procedure. CP-patients with osteoarthritis showed the lowest level of conscious processing and the highest level of guessing behavior. Patients with somatoform pain tended to react preconsciously to health threatening stimuli but overall showed a profile similar to that of controls who did not have CP. The impact of the threatening quality of stimuli on different levels of cognitive processing was weak. Depression did not mediate between the experience of pain and estimates of conscious and preconscious processing. Perspective: The impact of CP on preconscious and conscious cognitive processing depends on types and causes of pain. The experience of CP caused by inflammation or physical damage tends to reduce the probability of conscious processing and to provoke memory biases. CP in the context of a somatoform disorder seems to have less impact on cognitive functions
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