Miracle-dz/UniFAR-FTUs huggingface dataset:
Encoder Transfer for Attention-based Acoustic-to-word Speech Recognition

Acoustic-to-word speech recognition based on attention-based encoder-decoder models achieves better accuracies with much lower latency than the conventional speech recognition systems. However, acoustic-to-word models require a very large amount of training data and it is difﬁcult to prepare one for a new domain such as elderly speech. To address the problem, we pro-pose domain adaptation based on transfer learning with layer freezing. Layer freezing ﬁrst pre-trains a network with the source domain data, and then a part of parameters is re-trained for the target domain while the rest is ﬁxed. In the attention-based acoustic-to-word model, the encoder part is frozen to maintain the generality, and only the decoder part is re-trained to adapt to the target domain. This substantially allows for adaptation of the latent linguistic capability of the decoder to the target domain. Using a large-scale Japanese spontaneous speech corpus as source, the proposed method is applied to three target domains: a call center task and two voice search tasks by adults and by elderly. The models trained with the proposed method achieved better accuracy than the baseline models, which are trained from scratch or entirely re-trained with the target domain.
An Investigation of Mixup Training Strategies for Acoustic Models in ASR

Mixup is a recently proposed technique that creates virtual training examples by combining existing ones. It has been successfully used in various machine learning tasks. This paper focuses on applying mixup to automatic speech recognition (ASR). More speciﬁcally, several strategies for acoustic model training are investigated, including both conventional cross-entropy and novel lattice-free MMI models. Considering mixup as a method of data augmentation as well as regularization, we compare it with widely used speed perturbation and dropout techniques. Experiments on Switchboard-1, AMI and TED-LIUM datasets shows consistent improvement of word error rate up to 13% relative. Moreover, mixup is found to be particularly effective on test data mismatched to the training data.
Real- Time Hyperspectral Stereo Processing for the Generation of 3D Depth Information

We present a local stereo matching method for hyperspectral camera data, allowing multiple usage of camera hardware and imaging data such as for object classification or spectral analysis and multichannel input to the correspondence problem. The matching process combines correlation-based similarity measures for pixel windows utilizing all 16 spectral channels followed by a consistency check for disparity selection. We evaluate stereo-processing methods focusing on effectiveness and runtime of the processing on a CPU and analyze parallelization possibilities. Based on the results of the evaluation on the CPU, we implement the optimized stereo matching for images with 16 channels on a graphics processing unit (GPU) utilizing the Compute Unified Device Architecture (CUDA). The parallel processing of the calculation steps to obtain the disparity image on the GPU achieves more than $27\times$ speed up, resulting in calculation and post-processing of hyperspectral images with 8 – 13 Hz, depending on the selection of maximum disparity. The 3D reconstruction achieves a mean square error of 0.0267 m2 in distance measurements from 5 – 10 m.
Robust Acoustic Event Classification Using Bag-of-Visual-Words

This paper presents a novel Bag-of-Visual-Words (BoVW) approach, to represent the grayscale spectrograms of acoustic events. Such, BoVW representations are referred as histograms of visual features, used for Acoustic Event Classiﬁ-cation (AEC). Further, Chi-square distance between histograms of visual features evaluated, which generates kernel to Support Vector Machines (Chi-square SVM) classiﬁer. Evaluation of the proposed histograms of visual features together with Chi-square SVM classiﬁer is conducted on different categories of acoustic events from UPC-TALP corpora in clean and different noise conditions. Results show that proposed approach is more robust to noise and achieves improved recognition accuracy compared to other methods.
Inference-Invariant Transformation of Batch Normalization for Domain Adaptation of Acoustic Models

Batch normalization, or batchnorm, is a popular technique often used to accelerate and improve training of deep neural networks. When existing models that use this technique via batchnorm layers, are used as initial models for domain adaptation or transfer learning, the novel input feature distributions of the adapted domains, considerably change the batchnorm transformations learnt in the training mode from those which are applied in the inference mode. We empirically find that this mismatch can degrade the performance of domain adaptation for acoustic modeling. To mitigate this degradation, we propose an inference-invariant transformation of batch normalization, a method which reduces the mismatch between training mode and inference mode transformations without changing the inference results. This invariance property is achieved by adjusting the weight and bias terms of the batchnorm to compensate for differences in the mean and variance terms when using the adaptation data. Experimental results show that our proposed method performs the best on several acoustic model adaptation tasks with up to 5% relative improvement in recognition performances in both supervised and unsupervised domain adaptation settings.
Auditory Filterbank Learning for Temporal Modulation Features in Replay Spoof Speech Detection

In this paper, we present a standalone replay spoof speech detection (SSD) system to classify the natural vs. replay speech. The replay speech spectrum is known to be affected in the higher frequency range. In this context, we propose to exploit an auditory ﬁlterbank learning using Convolutional Restricted Boltzmann Machine (ConvRBM) with the pre-emphasized speech signals. Temporal modulations in amplitude (AM) and frequency (FM) are extracted from the ConvRBM subbands using the Energy Separation Algorithm (ESA). ConvRBM-based short-time AM and FM features are developed using cepstral processing, denoted as AM-ConvRBM-CC and FM-ConvRBM-CC. Proposed temporal modulation features performed better than the base-line Constant-Q Cepstral Coefﬁcients (CQCC) features. On the evaluation set, an absolute reduction of 7.48 % and 5.28 % in Equal Error Rate (EER) is obtained using AM-ConvRBM-CC and FM-ConvRBM-CC, respectively compared to our CQCC baseline. The best results are achieved by combining scores from AM and FM cues (0.82 % and 8.89 % EER for development and evaluation set, respectively). The statistics of AM-FM features are analyzed to understand the performance gap and complementary information in both the features.
Visual Recognition of Continuous Cued Speech Using a Tandem CNN-HMM Approach

This study addresses the problem of automatic recognition of Cued Speech (CS), a visual mode of communication for hearing impaired people in which a complete phonetic repertoire is obtained by combining lip movements with hand cues. In the proposed system, the dynamic of visual features extracted from lip and hand images using convolutional neural networks (CNN) are modeled by a set of hidden Markov models (HMM), for each phonetic context (tandem architecture). CNN-based feature extraction is compared to an unsupervised approach based on the principal component analysis. A novel temporal segmentation of hand streams is used to train CNNs efﬁciently. Different strategies for combining the extracted visual features within the HMM decoder are investigated. Experimental evaluation is carried on an audiovisual dataset (containing only continuous French sentences) recorded speciﬁcally for this study. In its best conﬁguration, and without exploiting any dictionary or language model, the proposed tandem CNN-HMM architecture is able to identify correctly more than 73 % of the phoneme ( 62 % when considering insertion errors).
Moving-Camera Video Surveillance in Cluttered Environments Using Deep Features

This paper deals with the challenging problem of visual anomaly detection in a cluttered environment using videos acquired with a moving camera. The anomalies considered are abandoned objects. A new method is proposed for comparing two videos (an anomaly-free reference video and a target one possibly with anomalies) by using convolutional neural networks as feature extractors for a subsequent anomaly-detection stage using a classifier. Two classifier strategies are considered, namely a fully-connected neural network and a random forest algorithm. Results for a comprehensive abandoned object database acquired with a moving camera in a cluttered environment indicate that the proposed architecture can match even the state-of-the-art algorithms in terms of object-detection performance, with a reduction in processing time of 80%.
Low Resource Acoustic-to-articulatory Inversion Using Bi-directional Long Short Term Memory

Estimating articulatory movements from speech acoustic features is known as acoustic-to-articulatory inversion (AAI). Large amount of parallel data from speech and articulatory motion is required for training an AAI model in a subject dependent manner, referred to as subject dependent AAI (SD-AAI). Electromagnetic articulograph (EMA) is a promising technology to record such parallel data, but it is expensive, time consuming and tiring for a subject. In order to reduce the demand for parallel acoustic-articulatory data in the AAI task for a subject, we, in this work, propose a subject-adaptative AAI method (SA-AAI) from an existing AAI model which is trained using large amount of parallel data from a ﬁxed set of subjects. Experiments are performed with 30 subjects’ acoustic-articulatory data and AAI is trained using BLSTM network to examine the amount of data needed from a new target subject for the SA-AAI to achieve an AAI performance equivalent to that of SD-AAI. Experimental results reveal that the proposed SA-AAI performs similar to that of the SD-AAI with ∼ 62.5% less train- ing data. Among different articulators, the SA-AAI performance for tongue articulators matches with the corresponding SD-AAI performance with only ∼ 12.5% of the data used for SD-AAI training.
Use of a Modified STROOP Test to Assess Color Discrimination Deficit in Parkinson's Disease

Objective: To objectively measure color vision dysfunction in idiopathic Parkinson's disease (iPD) using an easily administered, essentially free, modified Stroop test. Methods: Sixty-one iPD patients and 26 age-matched controls (HC) were enrolled after IRB approval and performed congruent (CST) and incongruent (IST) modified Stroop tests consisting of 40 words in 10 colors arranged in a 5 x 8 grid. The scorer was blinded to participant diagnosis. Errors on IST were defined as type 1 (written word reported rather than color) or type 2 (color reported different from the written word or its color). Results: The iPD group and the control group completed testing with similar CST performance. On the IST, 75.4% of iPD patients had type 2 errors (p = 0.001, OR 4.907, 95%CI 1.838–13.097) compared to 38.5% HC, with a positive predictive value of 82%. The mean number of type 2 errors was also higher in the iPD group, even with MoCA scores as a covariate in the analysis. Type 1 errors were not significantly different between the groups. A univariate logistic regression model with age, gender, MoCA, normalized IST completion time and the presence/absence of type 2 errors also resulted in type 2 errors as the only significant factor in the equation (p = 0.026). Conclusions: The modified Stroop test incorporated into the clinical evaluation of a patient may provide a quick and inexpensive objective measure of a non-motor feature of iPD, which could help in the clinical diagnosis of iPD in conjunction with the motor assessments currently used by neurologists.
A Voice Conversion Framework with Tandem Feature Sparse Representation and Speaker-Adapted WaveNet Vocoder

A voice conversion system typically consists of two modules, the feature conversion module that is followed by a vocoder. The exemplar-based sparse representation marks a success in feature conversion when we only have a very limited amount of training data. While parametric vocoder is generally designed to simulate the mechanics of the human speech generation process under certain simpliﬁcation assumptions, it doesn’t work consistently well for all target applications. In this paper, we study two effective ways to make use of the limited amount of training data for voice conversion. Firstly, we study a novel technique for sparse representation that augments the spectral features with phonetic information, or Tandem Feature. Secondly, we study the use of WaveNet vocoder that can be trained on multi-speaker and target speaker data to improve the vocoding quality. We evaluate that the proposed strategy with Tandem Feature and WaveNet vocoder, and show that it provides performance improvement consistently over the traditional sparse representations framework in objective and subjective evaluations.
Can Creative Computing Foster Growth Mindset?

Teacher training in computational thinking (CT) is becoming more and more important, as many countries are introducing CT at all K-12 school levels. Introductory programming courses are known to be diﬃcult, and some studies suggest they foster an entity theory of intelligence (ﬁxed mindset), reinforcing the idea that only some people have so-called “geek gene”. This is particularly dangerous if thought by future primary school teachers. We analyzed the eﬀects of an introductory course about computational thinking and creative computing with Scratch , and observed a statistically signiﬁcant increase of pre-service teachers’ growth mindset while observing a statistically signiﬁcant decrease in their computer anxiety. The structure of the course is detailed, with particular emphasis on some characteristics that may have determined growth mindset increase. Limitations of this exploratory study are discussed, and future work is depicted.
DLBCL Coast to Coast

Melania Tesio Institut Necker Enfants Malades, Paris, France Funding/support: None. The authors have indicated they have no potential conflicts of interest to disclose. Copyright © 2018 the Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the European Hematology Association. This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NCND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. HemaSphere (2018) 2:1(e23) Citation: Tesio M. DLBCL Coast to Coast. HemaSphere, 2018;2:1. http:// dx.doi. org/10.1097/ HS9.0000000000000023 1001 patient samples, a new study provides the first comprehensive characterization of diffuse large B-cell lymphoma.
Making Third Person Techniques Recognize First-Person Actions in Egocentric Videos

We focus on first-person action recognition from egocentric videos. Unlike third person domain, researchers have divided first-person actions into two categories: involving hand-object interactions and the ones without, and developed separate techniques for the two action categories. Further, it has been argued that traditional cues used for third person action recognition do not suffice, and egocentric specific features, such as head motion and handled objects have been used for such actions. Unlike the state-of-the-art approaches, we show that a regular two stream Convolutional Neural Network (CNN) with Long Short-Term Memory (LSTM) architecture, having separate streams for objects and motion, can generalize to all categories of first-person actions. The proposed approach unifies the feature learned by all action categories, making the proposed architecture much more practical. In an important observation, we note that the size of the objects visible in the egocentric videos is much smaller. We show that the performance of the proposed model improves after cropping and resizing frames to make the size of objects comparable to the size of ImageNet's objects. Our experiments on the standard datasets: GTEA, EGTEA Gaze+, HUJI, ADL, UTE, and Kitchen, proves that our model significantly outperforms various state-of-the-art techniques.
Whisper

This article presents Whisper, a fast and reliable protocol to flood small amounts of data into a multi-hop network. Whisper makes use of synchronous transmissions, a technique first introduced by the Glossy flooding protocol. In contrast to Glossy, Whisper does not let the radio switch from receive to transmit mode between messages. Instead, it makes nodes continuously transmit identical copies of the message and eliminates the gaps between subsequent transmissions. To this end, Whisper embeds the message to be flooded into a signaling packet that is composed of multiple packlets—where a packlet is a portion of the message payload that mimics the structure of an actual packet. A node must intercept only one of the packlets to detect that there is an ongoing transmission and that it should start forwarding the message. This allows Whisper to speed up the propagation of the flood and, thus, to reduce the overall radio-on time of the nodes. Our evaluation on the FlockLab testbed shows that Whisper achieves comparable reliability but 2× lower radio-on time than Glossy. We further show that by embedding Whisper in an existing data collection application, we can more than double the lifetime of the network.
Image Declipping with Deep Networks

We present a deep network to recover pixel values lost to clipping. The clipped area of the image is typically a uniform area of minimum or maximum brightness, losing image detail and color fidelity. The degree to which the clipping is visually noticeable depends on the amount by which values were clipped, and the extent of the clipped area. Clipping may occur in any (or all) of the pixel's color channels. Although clipped pixels are common and occur to some degree in almost every image we tested, current automatic solutions have only partial success in repairing clipped pixels and work only in limited cases such as only with overexposure (not under-exposure) and when some of the color channels are not clipped. Using neural networks and their ability to model natural images allows our neural network, DeclipNet, to reconstruct data in clipped regions producing state of the art results.
Diesseits von Eden

Abstract This study examines paratexts and images in works of primatology. In order to classify generic traits of primatographical publications, all paratexts, images and narrative positions of a large corpus of such monographs were registered. The analysis of these data allows for the determination of three distinct genres: scientific books, illustrated books and autobiographical/popular science books. The paratexts also reveal the strategies employed in the presentation of the books: They address a lay public, underline scientific objectivity or generate authenticity. The form of the texts indicate the audiences that the books address and enact an intimate relationship between non-human primates and human beings. Images showing researchers in close contact with non-human primates as well as paratexts addressing monkeys or calling for their preservation and conservation embed these field studies within a Christian iconography, invoke the life of saints or martyrs and appeal to the empathy of the readership.
Novel surgical techniques in female to male gender confirming surgery

The current management of female to male (FTM) gender confirmation surgery is based on the advances in neo phalloplasty, perioperative care and the knowledge of the female genital anatomy, as well as the changes that occur to this anatomy with preoperative hormonal changes in transgender population. Reconstruction of the neophallus is one of the most difficult elements in surgical treatment of female transsexuals. While there is a variety of available surgical techniques, their results are not equally acceptable to all patients. The preference for a particular surgical technique mostly depends on the patient’s desires and expectations. Nevertheless, the surgeon’s duty is to fully inform the patient about all the advantages and disadvantages, as well as all complications that might occur after surgical procedure—and even to talk them out of a desired surgical technique if there are contraindications. Metoidioplasty is a technically demanding surgical procedure used in FTM transsexuals who desire a gender reassignment surgery (GRS) without undergoing a complex, multi-staged surgical creation of an adult-sized phallus. Metoidioplasty is viable in cases where the clitoris seems large enough after androgen hormonal treatment. Since the clitoris plays the main role in female sexual satisfaction, its impact on the outcome of FTM transgender surgery is predictable. Various free flaps have been reported for total phalloplasty, such as radial forearm flap, latissimus dorsi flap, anterolateral tight flap, different abdominal wall flaps, free deltoid flap, scapular free flap, sensate osteocutaneous free fibula flap, tensor fasciae latae, deep epigastric artery perforator flap and dorsalis pedis flap. The fact that there are so many techniques for penile reconstruction in cases of penis absence, proves that none of the above mentioned techniques succeeded in achieving the ideal goals of penis reconstruction. We will emphasize the most commonly used surgical techniques in genital confirmation in FTM transsexuals with reference to respective eligibility criteria for each procedure.
Investigation on Joint Representation Learning for Robust Feature Extraction in Speech Emotion Recognition

Speech emotion recognition (SER) is a challenging task due to its difficulty in finding proper representations for emotion embedding in speech. Recently, Convolutional Recurrent Neural Network (CRNN), which is combined by convolution neural network and recurrent neural network, is popular in this field and achieves state-of-art on related corpus. However, most of work on CRNN only utilizes simple spectral information, which is not capable to capture enough emotion characteristics for the SER task. In this work, we investigate two joint representation learning structures based on CRNN aiming at capturing richer emotional information from speech. Cooperating the handcrafted high-level statistic features with CRNN, a two-channel SER system (HSF-CRNN) is developed to jointly learn the emotion-related features with better discriminative property. Furthermore, considering that the time duration of speech segment significantly affects the accuracy of emotion recognition, another two-channel SER system is proposed where CRNN features extracted from different time scale of spectrogram segment are used for joint representation learning. The systems are evaluated over Atypical Affect Challenge of ComParE2018 and IEMOCAP corpus. Experimental results show that our proposed systems outperform the plain CRNN.