Miracle-dz/UniFAR-FTUs huggingface dataset:
Speech Emotion Recognition 'in the Wild' Using an Autoencoder

Speech Emotion Recognition (SER) has been a challenging task on which researchers have been working for decades. Recently, Deep Learning (DL) based approaches have been shown to perform well in SER tasks; however, it has been noticed that their superior performance is limited to the distribution of the data used to train the model. In this paper, we present an analysis of using autoencoders to improve the generalisability of DL based SER solutions. We train a sparse autoencoder using a large speech corpus extracted from social media. Later, the trained encoder part of the autoencoder is reused as the input to a long short-term memory (LSTM) network, and the encoder-LSTM modal is re-trained on an aggregation of ﬁve commonly used speech emotion corpora. Our evaluation uses an unseen corpus in the training & validation stages to simulate ‘in the wild’ condition and analyse the generalisability of our solution. A performance comparison is carried out between the encoder based model and a model trained without an encoder. Our re-sults show that the autoencoder based model improves the un-weighted accuracy of the unseen corpus by 8%, indicating au-toencoder based pre-training can improve the generalisability of DL based SER solutions.
LAIX Corpus of Chinese Learner English: Towards a Benchmark for L2 English ASR

This paper introduces a corpus of Chinese Learner English containing 82 hours of L2 English speech by Chinese learners from all major dialect regions, collected through mobile apps developed by LAIX Inc. The LAIX corpus was created to serve as a benchmark dataset for evaluating Automatic Speech Recognition (ASR) performance on L2 English, the first of this kind as far as we know. The paper describes our effort to build the corpus, including corpus design, data selection and transcription. Multiple rounds of quality check were conducted in the transcription process. Transcription errors were analyzed in terms of error types, rounds of reviewing, and learners' proficiency levels. Word error rates of state-of-the-art ASR systems on the benchmark corpus were also reported.
SpeechMix - Augmenting Deep Sound Recognition Using Hidden Space Interpolations

This paper presents SpeechMix, a regularization and data augmentation technique for deep sound recognition. Our strategy is to create virtual training samples by interpolating speech samples in hidden space. SpeechMix has the potential to generate an inﬁnite number of new augmented speech samples since the combination of speech samples is continuous. Thus, it allows downstream models to avoid overﬁtting drastically. Unlike other mixing strategies that only work on the input space, we apply our method on the intermediate layers to capture a broader representation of the feature space. Through an extensive quantitative evaluation, we demonstrate the effectiveness of SpeechMix in comparison to standard learning regimes and previously applied mixing strategies. Furthermore, we highlight how different hidden layers contribute to the improvements in classiﬁcation using an ablation study.
Effects of Human Endothelial Progenitor Cell and Its Conditioned Medium on Oocyte Development and Subsequent Embryo Development

Human endothelial progenitor cells (EPCs) secrete numerous growth factors, and they have been applied to regenerative medicine for their roles in angiogenesis as well as neovascularization. Angiogenesis is one of the essential factors for the maturation of ovarian follicles; however, the physiological function of EPCs or their derivatives on in vitro culture systems has not been fully understood. The aim of this study was to evaluate the effectiveness of EPCs and their conditioned medium (EPC-CM) on oocyte development and subsequent embryo development. In the results, the oocyte development and subsequent embryo development were significantly improved in EPCs and the EPC-CM group. In addition, markedly increased levels of growth factors/cytokines, such as basic fibroblast growth factor (bFGF), vascular endothelial growth factor (VEGF), insulin growth factor-1 (IGF-1), interleukin-10 (IL-10), and epidermal growth factor (EGF), were observed in medium from the EPC-CM group. Additionally, EPC-CM after in vitro maturation (IVM) had significantly decreased reactive oxygen species (ROS) levels compared to those of other groups. Transcriptional levels of growth factor receptor-related genes (FGFR2, IGF1R) and anti-apoptotic-related gene (BCL2) were significantly upregulated in cumulus cells/oocytes from the EPC-CM group compared with those from the control. Furthermore, the expression levels of cumulus expansion-related genes (PTGS2, TNFAIP6, HAS2) and oocyte-maturation-related factors (GDF9, BMP15) were significantly enhanced in the EPC-CM group. Consequently, the present study provides the first evidence that EPC-CM contains several essential growth factors for oocyte development by regulating genes involved in oocyte maturation.
Joint Prediction of Punctuation and Disfluency in Speech Transcripts

Spoken language transcripts generated from Automatic speech recognition (ASR) often contain a large portion of disﬂuency and lack punctuation symbols. Punctuation restoration and dis-ﬂuency removal of the transcripts can facilitate downstream tasks such as machine translation, information extraction and syntactic analysis [1]. Various studies have shown the inﬂuence between these two tasks and thus performed modeling based on a multi-task learning (MTL) framework [2, 3], which learns general representations in the shared layers and separate representations in the task-speciﬁc layers. However, task dependencies are normally ignored in the task-speciﬁc layers. To model the dependencies of tasks, we propose an attention-based structure in the task-speciﬁc layers of the MTL framework incorporating the pretrained BERT (a state-of-art NLP-related model) [4]. Experimental results based on English IWSLT dataset and the Switchboard dataset show the proposed architecture outperforms the separate modeling methods as well as the traditional MTL methods.
Speaker Adaptive Training for Speech Recognition Based on Attention-Over-Attention Mechanism

In our previous work, we introduced a speaker adaptive training method based on frame-level attention mechanism for speech recognition, which has been proved an effective way to do speaker adaptive training. In this paper, we present an improved method by introducing the attention-over-attention mechanism. This attention module is used to further measure the contribution of each frame to the speaker embeddings in an utter-ance, and then generate an utterance-level speaker embedding to perform speaker adaptive training. Compared with the frame-level ones, the generated utterance-level speaker embeddings are more representative and stable. Experiments on both the Switchboard and AISHELL-2 tasks show that our method can achieve a relative word error rate reduction of approximately 8.0% compared with the speaker independent model, and over 6.0% compared with the traditional utterance-level d-vector-based speaker adaptive training method.
The sockeye salmon genome, transcriptome, and analyses identifying population defining regions of the genome

Sockeye salmon (Oncorhynchus nerka) is a commercially and culturally important species to the people that live along the northern Pacific Ocean coast. There are two main sockeye salmon ecotypes-the ocean-going (anadromous) ecotype and the fresh-water ecotype known as kokanee. The goal of this study was to better understand the population structure of sockeye salmon and identify possible genomic differences among populations and between the two ecotypes. In pursuit of this goal, we generated the first reference sockeye salmon genome assembly and an RNA-seq transcriptome data set to better annotate features of the assembly. Resequenced whole-genomes of 140 sockeye salmon and kokanee were analyzed to understand population structure and identify genomic differences between ecotypes. Three distinct geographic and genetic groups were identified from analyses of the resequencing data. Nucleotide variants in an immunoglobulin heavy chain variable gene cluster on chromosome 26 were found to differentiate the northwestern group from the southern and upper Columbia River groups. Several candidate genes were found to be associated with the kokanee ecotype. Many of these genes were related to ammonia tolerance or vision. Finally, the sex chromosomes of this species were better characterized, and an alternative sex-determination mechanism was identified in a subset of upper Columbia River kokanee.
Deep Scattering Power Spectrum Features for Robust Speech Recognition

Deep scattering spectrum consists of a cascade of wavelet transforms and modulus non-linearity. It generates features of different orders, with the ﬁrst order coefﬁcients approximately equal to the Mel-frequency cepstrum, and higher order coefﬁcients recovering information lost at lower levels. We investigate the effect of including the information recovered by higher order coefﬁcients on the robustness of speech recognition. To that end, we also propose a modiﬁcation to the original scattering transform tailored for noisy speech. In particular, instead of the modulus non-linearity we opt to work with power coefﬁcients and, therefore, use the squared modulus non-linearity. We quantify the robustness of scattering features using the word error rates of acoustic models trained on clean speech and evaluated using sets of utterances corrupted with different noise types. Our empirical results show that the second order scattering power spectrum coefﬁcients capture invariants relevant for noise robustness and that this additional information improves generalization to unseen noise conditions (almost 20% relative error reduction on AURORA 4). This ﬁnding can have important consequences on speech recognition systems that typically discard the second order information and keep only the ﬁrst order features (known for emulating MFCC and FBANK values) when representing speech.
An Utterance Verification System for Word Naming Therapy in Aphasia

Anomia (word finding difficulties) is the hallmark of aphasia an acquired language disorder, most commonly caused by stroke. Assessment of speech performance using picture naming tasks is therefore a key method for identification of the disorder and monitoring patient’s response to treatment interventions. Currently, this assessment is conducted manually by speech and language therapists (SLT). Surprisingly, despite advancements in ASR and artificial intelligence with technologies like deep learning, research on developing automated systems for this task has been scarce. Here we present an utterance verification system incorporating a deep learning element that classifies ‘correct’/’incorrect’ naming attempts from aphasic stroke patients. When tested on 8 native British-English speaking aphasics the system’s performance accuracy ranged between 83.6% to 93.6%, with a 10 fold cross validation mean of 89.5%. This performance was not only significantly better than one of the leading commercially available ASRs (Google speech-to-text service) but also comparable in some instances with two independent SLT ratings for the same dataset.
Intestinal microbiota composition of patients with Behçet's disease: differences between eye, mucocutaneous and vascular involvement. The Rheuma-BIOTA study.

OBJECTIVES Changes in microbiota composition affect the aetiology and patho-genesis of chronic diseases, including Behçet's disease (BD). However, no studies have analysed the potential gut microbiota changes among different clinical forms of BD. This study evaluated the intestinal microbiota composition of patients with BD and healthy controls and also compared differences between patients with BD with respect to eye, mucocutaneous, and vascular involvement. METHODS In this prospective cohort study, 27 patients diagnosed with BD according to the International Study Group criteria and 10 age- and sex-matched healthy controls were included. Detailed intestinal microbiota analysis was performed. RESULTS There were no differences between the BD group and the control group in terms of alpha and beta microbial diversity and abundance indices (p>0.05). Actinomyces, Libanicoccus, Collinsella, Eggerthella, Enetrohabdus, Catenibacterium, and Enterobacter were significantly higher in the BD group than in the control group. In addition, Bacteroides, Cricetibacter, Alistipes, Lachnospira, Dielma, Akkermansia, Sutterella, Anaerofilum, Ruminococcease-UCG007, Acetanaerobacterium, and Copropaacter were lower in the BD group than in the control group. When we compared three different system involvement (eye, mucocutaneous, and vascular), the linear discriminant analysis effective size revealed a difference for the following genera: Lachnospiraceae NK4A136 in the uveitis group; Dialister, Intestinomonas, and Marvinbryantia in the mucocutaneous group; and Gemella in the vascular group. CONCLUSIONS The composition of intestinal microbiota was significantly different in patients with BD compared with healthy adults. Ours is the first study to show differences in microbiota composition in isolated mucocutaneous, eye, and vascular involvement. These findings should be evaluated in a larger series.
Rapid Enhancement of NLP Systems by Acquisition of Data in Correlated Domains

In a generation where industries are going through a paradigm shift because of the rampant growth of deep learning, structured data plays a crucial role in the automation of various tasks. Textual structured data is one such kind which is extensively used in systems like chat bots and automatic speech recognition. Unfortunately, a majority of these textual data available is unstructured in the form of user reviews and feedback, social media posts etc. Automating the task of categorizing or clustering these data into meaningful domains will reduce the time and effort needed in building sophisticated human-interactive systems. In this paper, we present a web tool that builds a domain speciﬁc data based on a search phrase from a database of highly unstructured user utterances. We also show the usage of Elasticsearch database with custom indexes for full correlated text-search. This tool uses the open sourced Glove model combined with cosine similarity and performs a graph based search to provide semantically and syntactically meaningful corpora. In the end, we discuss its applications with respect to natural language processing.
Single-Channel Speech Enhancement by Subspace Affinity Minimization

In data-driven speech enhancement frameworks, learning informative representations is crucial to obtain a high-quality estimate of the target speech. State-of-the-art speech enhancement methods based on deep neural networks (DNN) commonly learn a single embedding from the noisy input to predict clean speech. This compressed representation inevitably contains both noise and speech information leading to speech distortion and poor noise reduction performance. To alleviate this issue, we proposed to learn from the noisy input separate embeddings for speech and noise and introduced a subspace afﬁnity loss function to prevent information leaking between the two representations. We rigorously proved that minimizing this loss function yields maximally uncorrelated speech and noise representations, which can block information leaking. We empirically showed that our proposed framework outperforms traditional and state-of-the-art speech enhancement methods in various unseen nonstationary noise environments. Our results suggest that learning uncorrelated speech and noise embeddings can improve noise reduction and reduces speech distortion in speech enhancement applications.
Anti-Zo antibodies in Japanese myositis patients detected by a newly developed ELISA.

OBJECTIVES The myositis-specific autoantibodies that characterise certain forms of idiopathic inflammatory myopathy (IIM) are useful for diagnosing dermatomyositis (DM) / polymyositis (PM) and predicting its prognosis. The autoantibody to phenylalanyl-tRNA synthetase (anti-Zo) has been identified as a disease marker antibody for anti-synthetase syndrome only in a UK cohort. Here we aim to establish an ELISA for the measurement of anti-Zo and to characterise the clinical features of Japanese patients who have this autoantibody. METHODS Anti-Zo was investigated by immunoprecipitation with recombinant phenylalanyl-tRNA synthetase α/β proteins. The results were confirmed by immunoprecipitation-Western blotting with cell extract. Sera from patients with DM/PM (n=224) were screened by an ELISA with the recombinant proteins. Medical records were retrospectively reviewed to obtain detailed information on the clinical phenotypes of the anti-Zo-positive patients. RESULTS Only two male patients were confirmed to have anti-Zo. Both patients had fever, myopathy, interstitial lung disease, and mechanic's hands, and these clinical features are consistent with those of anti-synthetase syndrome. Another patient's serum showed a higher level than the cut-off value for anti-phenylalanyl-tRNA synthetase α by our in-house ELISA, but was judged to be negative for anti-Zo by immunoprecipitation-Western blotting. CONCLUSIONS This is the first report of anti-Zo-positive IIM patients from Asia. Although Japanese patients with anti-Zo have a clinical phenotype similar to that of Caucasian patients, further large cohort studies are necessary to confirm the frequency of anti-Zo in Japanese IIM patients. Our newly developed ELISA should be validated for sensitivity and specificity in large cohorts.
Rethinking the link between cognitive sophistication and politically motivated reasoning.

Partisan disagreement over policy-relevant facts is a salient feature of contemporary American politics. Perhaps surprisingly, such disagreements are often the greatest among opposing partisans who are the most cognitively sophisticated. A prominent hypothesis for this phenomenon is that cognitive sophistication magnifies politically motivated reasoning-commonly defined as reasoning driven by the motivation to reach conclusions congenial to one's political group identity. Numerous experimental studies report evidence in favor of this hypothesis. However, in the designs of such studies, political group identity is often confounded with prior factual beliefs about the issue in question; and, crucially, reasoning can be affected by such beliefs in the absence of any political group motivation. This renders much existing evidence for the hypothesis ambiguous. To shed new light on this issue, we conducted three studies in which we statistically controlled for people's prior factual beliefs-attempting to isolate a direct effect of political group identity-when estimating the association between their cognitive sophistication, political group identity, and reasoning in the paradigmatic study design used in the literature. We observed a robust direct effect of political group identity on reasoning but found no evidence that cognitive sophistication magnified this effect. In contrast, we found fairly consistent evidence that cognitive sophistication magnified a direct effect of prior factual beliefs on reasoning. Our results suggest that there is currently a lack of clear empirical evidence that cognitive sophistication magnifies politically motivated reasoning as commonly understood and emphasize the conceptual and empirical challenges that confront tests of this hypothesis. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
Cloning and Functional Characterization of Dihydroflavonol 4-Reductase Gene Involved in Anthocyanin Biosynthesis of Chrysanthemum

Dihydroflavonol 4-reductase (DFR) catalyzes a committed step in anthocyanin and proanthocyanidin biosynthesis by reducing dihydroflavonols to leucoanthocyanidins. However, the role of this enzyme in determining flower color in the economically important crop chrysanthemum (Chrysanthemum morifolium Ramat.) is unknown. Here, we isolated cDNAs encoding DFR from two chrysanthemum cultivars, the white-flowered chrysanthemum “OhBlang” (CmDFR-OB) and the red-flowered chrysanthemum “RedMarble” (CmDFR-RM) and identified variations in the C-terminus between the two sequences. An enzyme assay using recombinant proteins revealed that both enzymes catalyzed the reduction of dihydroflavonol substrates, but CmDFR-OB showed significantly reduced DFR activity for dihydrokaempferol (DHK) substrate as compared with CmDFR-RM. Transcript levels of anthocyanin biosynthetic genes were consistent with the anthocyanin contents at different flower developmental stages of both cultivars. The in planta complementation assay, using Arabidopsis thaliana dfr mutant (tt3-1), revealed that CmDFR-RM, but not CmDFR-OB, transgenes restored defective anthocyanin biosynthesis of this mutant at the seedling stage, as well as proanthocyanidin biosynthesis in the seed. The difference in the flower color of two chrysanthemums can be explained by the C-terminal variation of CmDFR combined with the loss of CmF3H expression during flower development.
Mind the Gap! Decoupling Between Policy and Practice in the Policing of Illegal Wildlife Trade

Despite numerous promises and pledges at national and international levels to confront what many acknowledge as a crisis, illegal trade in wild plants and animals continues to grow and diversify. Empirical research conducted in Norway and Uganda from 2013 to 2015 indicates that despite the different circumstances in which law enforcement operates in the two countries, policing agents face a number of comparable challenges. Drawing on institutional theory the paper argues that decoupling, that is, gaps between official policies and daily work activities within the policing organizations, compromises enforcement in both countries. Challenges stem from conflicting demands, poor resources and want of guidelines that oblige officers to prioritize the control of illegal wildlife trade in practice.
Identification of Emotional Expression With Cancer Survivors: Validation of Linguistic Inquiry and Word Count

Background Given the high volume of text-based communication such as email, Facebook, Twitter, and additional web-based and mobile apps, there are unique opportunities to use text to better understand underlying psychological constructs such as emotion. Emotion recognition in text is critical to commercial enterprises (eg, understanding the valence of customer reviews) and to current and emerging clinical applications (eg, as markers of clinical progress and risk of suicide), and the Linguistic Inquiry and Word Count (LIWC) is a commonly used program. Objective Given the wide use of this program, the purpose of this study is to update previous validation results with two newer versions of LIWC. Methods Tests of proportions were conducted using the total number of emotion words identified by human coders for each emotional category as the reference group. In addition to tests of proportions, we calculated F scores to evaluate the accuracy of LIWC 2001, LIWC 2007, and LIWC 2015. Results Results indicate that LIWC 2001, LIWC 2007, and LIWC 2015 each demonstrate good sensitivity for identifying emotional expression, whereas LIWC 2007 and LIWC 2015 were significantly more sensitive than LIWC 2001 for identifying emotional expression and positive emotion; however, more recent versions of LIWC were also significantly more likely to overidentify emotional content than LIWC 2001. LIWC 2001 demonstrated significantly better precision (F score) for identifying overall emotion, negative emotion, and anxiety compared with LIWC 2007 and LIWC 2015. Conclusions Taken together, these results suggest that LIWC 2001 most accurately reflects the emotional identification of human coders.