Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting Collaborative Task Performance Using Graph Interlocutor Acoustic Network in Small Group Interaction

Recent works have demonstrated that the integration of group-level personality and vocal behaviors can provide enhanced prediction power on task performance for small group interactions. In this work, we propose that the impact of member personality for task performance prediction in groups should be explicitly modeled from both intra and inter -group perspectives. Specifically, we propose a Graph Interlocutor Acoustic Network (G-IAN) architecture that jointly learns the relationship between vocal behaviors and personality attributes with intra-group attention and inter-group graph convolutional layer. We evaluate our proposed G-IAN on two group interaction databases and achieve 78.4% and 72.2% group performance classiﬁcation accuracy, which outperforms the baseline model that models vocal behavior only by 14% absolute. Further, our analysis shows that Agreeableness and Conscientiousness demonstrate a clear positive impact in our model that leverages the inter-group personality structure for enhanced task performance prediction.
Exploration of Acoustic and Lexical Cues for the INTERSPEECH 2020 Computational Paralinguistic Challenge

In this paper, we investigate various acoustic features and lexical features for the INTERSPEECH 2020 Computational Par-alinguistic Challenge. For the acoustic analysis, we show that the proposed FV-MFCC feature is very promising, which has very strong prediction power on its own, and can also provide complementary information when fused with other acoustic features. For the lexical representation, we ﬁnd that the corpus-dependent TF.IDF feature is by far the best representation. We also explore several model fusion techniques to combine different modalities together, and propose novel SVM models to aggregate the chunk-level predictions to the narrative-level predictions based on the chunk-level decision functionals. Finally we discuss the potential for improving prediction by combining the lexical and acoustic modalities together, and we ﬁnd that fusion of lexical and acoustic modalities do not lead to consistent improvements over elderly Arousal, but substantially improve over the Valence. Our methods signiﬁcantly outperform the ofﬁcial baselines on the test set in the participated Mask and Elderly Sub-challenges. We obtain an UAR of 75 . 1% , 54 . 3% , and 59 . 0% on the Mask, Elderly Arousal and Valence prediction tasks respectively.
Phase Based Spectro-Temporal Features for Building a Robust ASR System

Spectro-temporal feature extraction has shown its robustness in the ﬁeld of speech recognition. However, these features are derived from magnitude spectrum of the complex Fourier Transform (FT). In this work, we investigate to see if phase information can substitute magnitude based spectro-temporal features. We compared with different state of art phase spectrum and evaluated its performance. The experiments are carried out in different noisy environments. We found Modiﬁed Group Delay (MODGD) spectrum to closely resemble the structure of power spectrum. A relative performance difference of 0.03% on average is observed for the MODGD spectro-temporal features compared to the magnitude based features. The analysis showed that phase can indeed carry equivalent or complementary information to magnitude based spectro-temporal features.
Effect of Microphone Position Measurement Error on RIR and its Impact on Speech Intelligibility and Quality

Room Impulse Response (RIR) measurement is done using a microphone and loudspeaker pair and is prone to error due to error in measurement of the microphone position. In literature, the adverse impact of ambient noise on RIR measurement is mostly explored. However, the impact of microphone position error on full, early and late RIR measurements have never been explored. In this paper, we investigate the error in RIR introduced due to error in measurement of the microphone position. We also study the impact of this on the quality and intelligibility of speech. Our analysis shows that the impact of error in micro-phone position measurement on RIR is as adverse as that of the ambient noise.
AffectivelyVR: Towards VR Personalized Emotion Recognition

We present AffectivelyVR, a personalized real-time emotion recognition system in Virtual Reality (VR) that enables an emotion-adaptive virtual environment. We used off-the-shelf Electroencephalogram (EEG) and Galvanic Skin Response (GSR) physiological sensors to train user-specific machine learning models while exposing users to affective 360° VR videos. Since emotions are largely dependent on interpersonal experiences and expressed in different ways for different people, we personalize the model instead of generalizing it. By doing this, we achieved an emotion recognition rate of 96.5% using the personalized KNN algorithm, and 83.7% using the generalized SVM algorithm.
Generative Adversarial Network Based Acoustic Echo Cancellation

Generative adversarial networks (GANs) have become a popular research topic in speech enhancement like noise suppression. By training the noise suppression algorithm in an adversarial scenario, GAN based solutions often yield good performance. In this paper, a convolutional recurrent GAN architecture (CRGAN-EC) is proposed to address both linear and nonlinear echo scenarios. The proposed architecture is trained in frequency domain and predicts the time-frequency (TF) mask for the target speech. Several metric loss functions are deployed and their influence on echo cancellation performance is studied. Experimental results suggest that the proposed method outperforms the existing methods for unseen speakers in terms of echo return loss enhancement (ERLE) and perceptual evaluation of speech quality (PESQ). Moreover, multiple metric loss functions provide more freedom to achieve specific goals, e.g., more echo suppression or less distortion
Spatial Resolution of Early Reflection for Speech and White Noise

In virtual auditory display, the accurate simulation of early reflection is helpful to guarantee audio fidelity and enhance immersion. However, the early reflection may not be easily distinguished from the direct sound due to the masking effect. This work investigated the spatial resolution of early reflection for speech and white noise under different conditions, in which three-down-one-up adaptive strategy with three-interval-three-alternative forced-choice (3I-3AFC) was employed. Results show that, for both speech and white noise, the spatial resolution of early reflection decreases with the increasing deviation of reflection orientation relative to the direct sound, and has no relationship with the time delay; Moreover, the spatial resolution of early reflection for speech is always lower than that for white noise under the same condition.
Dynamic Speaker Representations Adjustment and Decoder Factorization for Speaker Adaptation in End-to-End Speech Synthesis

End-to-end speech synthesis can reach high quality and naturalness with low-resource adaptation data. However, the generalization of out-domain texts and the improving modeling accuracy of speaker representations are still challenging tasks. The limited adaptation data leads to unacceptable errors and low similarity of the synthetic speech. In this paper, both speaker representations modeling and acoustic model structure are improved for the speaker adaptation task. On the one hand, compared with the conventional methods that focused on using ﬁxed global speaker representations, the attention gating is proposed to adjust speaker representations dynamically based on the attended context and prosody information, which can describe more pronunciation characteristics in phoneme level. On the other hand, to improve the robustness and avoid over-ﬁtting, the decoder model is factored into average-net and adaptation-net, which are designed for learning speaker independent acoustic features and target speaker timbre imitation respectively. And the context discriminator is pre-trained by large ASR data to supervise the average-net generating proper speaker independent acoustic features for different phoneme. Experimental results on Mandarin dataset show that proposed methods lead to an improvement on intelligibility, naturalness and similarity.
Reversible silencing of endogenous receptors in intact brain tissue using 2-photon pharmacology

Significance Two-photon fluorescent probes to image intracellular calcium and other biochemical processes have revolutionized cell biology. In neurobiology, 2-photon calcium indicators produce signals that indirectly reflect action potential firing in neurons deep in the brain, and have become essential to record and correlate large-scale neural activity at high spatiotemporal resolution. Here we show that 2-photon excitable, diffusible photoswitches allow the activation and deactivation of neurotransmitter receptors with higher subtype-specificity and spatial resolution compared with previous molecular tools, and combined with calcium indicators enables to both control and readout activity using 2-photon excitation. Specifically, we used a potent and selective antagonist to switch on/off at the micrometer scale the activity of mGlu5 receptors expressed endogenously in neurons and astrocytes of intact brain tissue. The physiological activity of proteins is often studied with loss-of-function genetic approaches, but the corresponding phenotypes develop slowly and can be confounding. Photopharmacology allows direct, fast, and reversible control of endogenous protein activity, with spatiotemporal resolution set by the illumination method. Here, we combine a photoswitchable allosteric modulator (alloswitch) and 2-photon excitation using pulsed near-infrared lasers to reversibly silence metabotropic glutamate 5 (mGlu5) receptor activity in intact brain tissue. Endogenous receptors can be photoactivated in neurons and astrocytes with pharmacological selectivity and with an axial resolution between 5 and 10 µm. Thus, 2-photon pharmacology using alloswitch allows investigating mGlu5-dependent processes in wild-type animals, including synaptic formation and plasticity, and signaling pathways from intracellular organelles.
Loss of COMT activity reduces lateral root formation and alters the response to water limitation in sorghum brown midrib (bmr) 12 mutant.

Lignin is a key target for modifying lignocellulosic biomass for efficient biofuel production. Brown midrib 12 (Bmr12) encodes the sorghum caffeic acid O-methyltransferase (COMT) and is one of the key enzymes in monolignol biosynthesis. Loss of function mutations in COMT reduces syringyl (S) lignin subunits and improves biofuel conversion rate. Although lignin plays an important role in maintaining cell wall integrity of xylem vessels, physiological and molecular consequences due to loss of COMT on root growth and adaptation to water deficit remain unexplored. We addressed this gap by evaluating the root morphology, anatomy and transcriptome of bmr12 mutant. The mutant had reduced lateral root density (LRD) and altered root anatomy and response to water limitation. The wildtype exhibits similar phenotypes under water stress, suggesting that bmr12 may be in a water deficit responsive state even in well-watered conditions. bmr12 had increased transcript abundance of genes involved in (a)biotic stress response, gibberellic acid (GA) biosynthesis and signaling. We show that bmr12 is more sensitive to exogenous GA application and present evidence for the role of GA in regulating reduced LRD in bmr12. These findings elucidate the phenotypic and molecular consequences of COMT deficiency under optimal and water stress environments in grasses.
Six-minute stepper test in hospitalized elderly patients: Convergent validity, test-retest reliability and safety

Objective To evaluate the convergent validity of the six-minute stepper test (6MST) with the variables used in the diagnosis of sarcopenia (appendicular muscle mass, handgrip strength and six-meter gait speed test), as well as to evaluate test-retest reliability and safety when applied to hospitalized elderly patients. Finally, we aimed to compare the performance in the 6MST between hospitalized elderly patients and healthy elderly from the community. Materials and methods Observational and cross-sectional study. Elderly patients admitted to a private hospital and healthy elderly from the community were recruited. On the first day, the patients included underwent the following assessments: anthropometric, handgrip strength (HGS), six-meter gait speed test (6GST) and 6MST. On the second day, before breakfast, patients underwent body composition assessment. The healthy elderly were evaluated on a single day and performed only anthropometric assessment and 6MST. Results 30 hospitalized patients (age 71.0±7.9 years) and 15 healthy elderly (age 68.1±5.8 years) were included. There was a high correlation of 6MST with 6GST (r = 0.78; p<0.001), moderate correlation with HGS (r = 0.59; p<0.001) and low correlation with appendicular muscle mass (r = 0.45; p = 0.01). There was no statistical difference between the first and second 6MST performed by hospitalized elderly (196.2±91.0 cycles vs. 191.3±103.7 cycles; p = 0.66), in addition to an excellent agreement between these measures (ICC = 0.90; 95% IC 0.78–0.95). Only one adverse event (3.3%) occurred in the sample. Conclusion 6MST showed convergent validity with the functional variables used in the diagnosis of sarcopenia. In addition, excellent test-retest reliability was observed, which indicates the need for a single assessment in hospitalized elderly patients. The prevalence of adverse events during the application of the test is low, without resulting in clinical symptoms; therefore, the test is considered safe for this population. In addition, hospitalized elderly patients perform worse in the 6MST compared to healthy elderly from the community.
Lattice-Free Maximum Mutual Information Training of Multilingual Speech Recognition Systems

Multilingual acoustic model training combines data from multiple languages to train an automatic speech recognition system. Such a system is beneﬁcial when training data for a target language is limited. Lattice-Free Maximum Mutual Information (LF-MMI) training performs sequence discrimination by introducing competing hypotheses through a denominator graph in the cost function. The standard approach to train a multilingual model with LF-MMI is to combine the acoustic units from all languages and use a common denominator graph. The resulting model is either used as a feature extractor to train an acoustic model for the target language or directly ﬁne-tuned. In this work, we propose a scalable approach to train the multilingual acoustic model using a typical multitask network for the LF-MMI framework. A set of language-dependent denominator graphs is used to compute the cost function. The proposed approach is evaluated under typical multilingual ASR tasks us-ing GlobalPhone and BABEL datasets. Relative improvements up to 13.2% in WER are obtained when compared to the corresponding monolingual LF-MMI baselines. The implementation is made available as a part of the Kaldi speech recognition toolkit.
Structural Analysis of Merkel Cell Polyomavirus (MCPyV) Viral Capsid Protein 1 (VP1) in HIV-1 Infected Individuals

Merkel cell polyomavirus (MCPyV) viral protein 1 (VP1) is the capsid protein that mediates virus attachment to host cell receptors and is the major immune target. Given the limited data on MCPyV VP1 mutations, the VP1 genetic variability was examined in 100 plasma and 100 urine samples from 100 HIV+ individuals. Sequencing of VP1 DNA in 17 urine and 17 plasma specimens, simultaneously MCPyV DNA positive, revealed that 27 samples displayed sequences identical to VP1 of MCC350 strain. VP1 from two urine specimens had either Thr47Ser or Ile115Phe substitution, whereas VP1 of one plasma contained Asp69Val and Ser251Phe substitutions plus deletion (∆) of Tyr79. VP1 DNA in the remaining samples had mutations encoding truncated protein. Three-dimensional prediction models revealed that Asp69Val, Ser251Phe, and Ile115Phe caused neutral effects while Thr47Ser and Tyr79∆ produced a deleterious effect reducing VP1 stability. A549 cells infected with urine or plasma samples containing full-length VP1 variants with substitutions, sustained viral DNA replication and VP1 expression. Moreover, medium harvested from these cells was able to infect new A549 cells. In cells infected by samples with truncated VP1, MCPyV replication was hampered. In conclusion, MCPyV strains with unique mutations in the VP1 gene are circulating in HIV+ patients. These strains display altered replication efficiency compared to the MCC350 prototype strain in A549 cells.
In-Built N+ Pocket Electrically Doped Tunnel FET With Improved DC and Analog/RF Performance

In this paper, we present an in-built N+ pocket electrically doped tunnel FET (ED-TFET) based on the polarity bias concept that enhances the DC and analog/RF performance. The proposed device begins with a MOSFET like structure (n-p-n) with a control gate (CG) and a polarity gate (PG). The PG is biased at −0.7 V to induce a P+ region at the source side, leaving an N+ pocket between the source and the channel. This technique yields an N+ pocket that is realized in the in-built architecture and removes the need for additional chemical doping. Calibrated 2-D simulations have demonstrated that the introduction of the N+ pocket yields a higher ION and a steeper average subthreshold swing when compared to conventional ED-TFET. Further, a local minimum on the conduction band edge (EC) curve at the tunneling junction is observed, leading to a dramatic reduction in the tunneling width. As a result, the in-built N+ pocket ED-TFET significantly improves the DC and analog/RF figure-of-merits and, hence, can serve as a better candidate for low-power applications.
Adult-onset temporal lobe epilepsy suspicious for autoimmune pathogenesis: Autoantibody prevalence and clinical correlates

Temporal lobe adult-onset seizures (TAOS) related to autoimmunity represent an increasingly recognized disease syndrome within the spectrum of epilepsies. In this context, certain autoantibodies (autoABs) were often associated with limbic encephalitis (LE). Here, we aimed to gain insights into (a) the distribution of ‘neurological’ autoABs (neuroABs, defined as autoABs targeting neuronal surface structures or ‘onconeuronal’ ABs or anti-glutamate acid decarboxylase 65 (GAD65) autoABs) in a large consecutive TAOS patient cohort, to characterize (b) clinical profiles of seropositive versus seronegative individuals and to find (c) potential evidence for other autoABs. Blood sera/cerebrospinal fluid (CSF) of TAOS patients (n = 800) and healthy donors (n = 27) were analyzed for neuroABs and screened for other autoABs by indirect immunofluorescence on hippocampal/cerebellar sections and immunoblots of whole brain and synaptosome lysates. Serological results were correlated with clinico-neuropsychological features. 13% of TAOS patients (n = 105) were neuroAB+, with anti-GAD65 and anti-N-methyl-D-aspartate receptors (NMDAR) as most frequent autoABs in this group. In our screening tests 25% of neuroAB- patients (n = 199) were positive (screening+), whereas all control samples were negative (n = 27). Intriguingly, key clinico-neuropsychological characteristics including magnetic resonance imaging (MRI) findings, epileptiform electroencephalographic (EEG) activity, and inflammatory cellular infiltrates in CSF were shared to a greater extent by neuroAB+ with neuroAB-/screening+ patients than with neuroAB-/screening- patients. Serological testing in a large consecutive TAOS patient series revealed seropositivity for anti-GAD65 autoABs as the most frequent neuroAB. Intriguingly, neuroAB+ individuals were virtually indistinguishable from neuroAB-/screening+ patients in several major clinical features. In contrast, neuroAB-/screening- TAOS patients differed in many parameters. These data support the potential presence of so far unrecognized autoABs in patients with TAOS.
Deep Template Matching for Small-Footprint and Configurable Keyword Spotting

Keyword spotting (KWS) is a very important technique for human–machine interaction to detect a trigger phrase and voice commands. In practice, a popular demand for KWS is to conveniently de ﬁ ne the keywords by consumers or device vendors. In this paper, we propose a novel template matching approach for KWS based on end-to-end deep learning method, which utilizes an attention mechanism to match the input voice to the keyword templates in high-level feature space. The proposed approach only requires very limited voice samples (at least only one sample) to register a new keyword without any retraining. We conduct experiments on the publicly available Google speech commands dataset. The experimental results demonstrate that our method outperforms baseline methods while allowing for a ﬂ ex-ible con ﬁ guration.
Prediction of Suicide-Related Events by Analyzing Electronic Medical Records from PTSD Patients with Bipolar Disorder

Around 800,000 people worldwide die from suicide every year and it’s the 10th leading cause of death in the US. It is of great value to build a mathematic model that can accurately predict suicide especially in high-risk populations. Several different ML-based models were trained and evaluated using features obtained from electronic medical records (EMRs). The contribution of each feature was calculated to determine how it impacted the model predictions. The best-performing model was selected for analysis and decomposition. Random forest showed the best performance with true positive rates (TPR) and positive predictive values (PPV) of greater than 80%. The use of Sertraline, Fentanyl, Aripiprazole, Lamotrigine, and Tramadol were strong indicators for no SREs within one year. The use of Haloperidol, Trazodone and Citalopram, a diagnosis of autistic disorder, schizophrenic disorder, or substance use disorder at the time of a diagnosis of both PTSD and bipolar disorder, predicted the onset of SREs within one year. Additional features with potential protective or hazardous effects for SREs were identified by the model. We constructed an ML-based model that was successful in identifying patients in a subpopulation at high-risk for SREs within a year of diagnosis of both PTSD and bipolar disorder. The model also provides feature decompositions to guide mechanism studies. The validation of this model with additional EMR datasets will be of great value in resource allocation and clinical decision making.
Towards Silent Paralinguistics: Deriving Speaking Mode and Speaker ID from Electromyographic Signals

Silent Computational Paralinguistics (SCP) – the assessment of speaker states and traits from non-audibly spoken communication – has rarely been targeted in the rich body of either Computational Paralinguistics or Silent Speech Processing. Here, we provide ﬁrst steps towards this challenging but potentially highly rewarding endeavour: Paralinguistics can enrich spoken language interfaces, while Silent Speech Processing enables conﬁdential and unobtrusive spoken communication for every-body, including mute speakers. We approach SCP by using speech-related biosignals stemming from facial muscle activities captured by surface electromyography (EMG). To demonstrate the feasibility of SCP, we select one speaker trait (speaker identity) and one speaker state (speaking mode). We introduce two promising strategies for SCP: (1) deriving paralinguistic speaker information directly from EMG of silently produced speech versus (2) ﬁrst converting EMG into an audible speech signal followed by conventional computational paralinguistic methods. We compare traditional feature extraction and decision making approaches to more recent deep representation and transfer learning by convolutional and recurrent neural networks, using openly available EMG data. We ﬁnd that paralinguistics can be assessed not only from acoustic speech but also from silent speech captured by EMG.