Miracle-dz/UniFAR-FTUs huggingface dataset:
Complex-Valued Variational Autoencoder: A Novel Deep Generative Model for Direct Representation of Complex Spectra

In recent years, variational autoencoders (VAEs) have been attracting interest for many applications and generative tasks. Although the VAE is one of the most powerful deep generative models, it still has difficulty representing complex-valued data such as the complex spectra of speech. In speech synthesis, we usually use the VAE to encode Mel-cepstra, or raw amplitude spectra, from a speech signal into normally distributed latent features and then synthesize the speech from the reconstruction by using the Griffin-Lim algorithm or other vocoders. Such inputs are originally calculated from complex spectra but lack the phase information, which leads to degradation when recovering speech. In this work, we propose a novel generative model to directly encode the complex spectra by extending the conventional VAE. The proposed model, which we call the complexvalued VAE (CVAE), consists of two complex-valued neural networks (CVNNs) of an encoder and a decoder. In the CVAE, not only the inputs and the parameters of the encoder and decoder but also the latent features are defined as complex-valued to preserve the phase information throughout the network. The results of our speech encoding experiments demonstrated the effectiveness of the CVAE compared to the conventional VAE in both objective and subjective criteria.
Effect of Spectral Complexity Reduction and Number of Instruments on Musical Enjoyment with Cochlear Implants

Although speech recognition technology for cochlear implants has continued to improve, music accessibility remains a challenge. Previous studies have shown that cochlear implant users may prefer listening to music that has been reengineered to be less complex. In this paper, we consider the combined effect of spectral complexity reduction and number of instruments playing on musical enjoyment with cochlear implants. Nine normal hearing listeners rated 200 10-second music samples on three enjoyment modalities (musicality, pleasantness, and naturalness) with and without the use of cochlear implant simulation. The music samples included 20 versions of the song “Twinkle Twinkle Little Star” synthesized using one of five different instruments and with one to four instruments playing at once. The remaining 180 versions were created by reducing each sample’s spectral complexity to nine different levels using principal component analysis. The results showed a preference for less amounts of spectral complexity reduction for samples without cochlear implant simulation (P<.001), as well as a preference for fewer instruments for samples with cochlear implant simulation (P<.001). However, spectral complexity reduction was not a significant factor for samples with cochlear implant simulation, and a significant interaction effect between spectral complexity reduction and number of instruments was not found.
Speech Enhancement with Stochastic Temporal Convolutional Networks

We consider the problem of speech modeling in speech enhancement. Recently, deep generative approaches based on variational autoencoders have been proposed to model speech spectrograms. However, these approaches are based either on hierarchical or temporal dependencies of stochastic latent variables. In this paper, we propose a generative approach to speech enhancement based on a stochastic temporal convolutional network, which combines both hierarchical and temporal dependencies of stochastic variables. We evaluate our method with real recordings of different noisy environments. The proposed speech enhancement method outperforms a previous non-sequential approach based on feed-forward fully-connected networks in terms of speech distortion, instrumental speech quality and intelligibility. At the same time, the computational cost of the proposed generative speech model remains feasible, due to inherent parallelism of the convolutional architecture
LCCSS: A Similarity Metric for Identifying Similar Test Code

Test code maintainability is a common concern in software testing. In order to achieve good maintainability, test methods should be clearly structured, well named, small in size, and, mainly, test code duplication should be avoided. Several strategies exist to avoid test code duplication, such as implicit setup and delegated setup. However, prior to applying these strategies, first it is necessary to identify the duplicate code, which can be a time-consuming task. To address this problem, we automate the identification of duplicate test code through the application of code similarity metrics. We propose a novel similarity metric, called Longest Common Contiguous Start Sub-Sequence (LCCSS), to identify refactoring candidates. LCCSS is a metric used to measure similarity between pairs of tests. The most similar pairs are reported as strong candidates to be refactored through the implicit setup strategy. We also develop a framework, called Róża, that can use different similarity metrics to identify test code duplication. An experiment shows that LCCSS and Simian, a clone detection tool, have both identified pairs of tests to be refactored through the implicit setup strategy with maximum precision in all the eleven standard recall levels. But, unlike Simian, LCCSS does not need to be calibrated for each project.
Microphone Array Post-Filter for Target Speech Enhancement Without a Prior Information of Point Interferers

The post-ﬁlter for microphone array speech enhancement can effectively suppress noise including point interferers. However, the suppression of point interferers relies on the accurate estimation of the number and directions of point interferers, which is a difﬁcult task in practical situations. In this paper, we pro-pose a post-ﬁltering algorithm, which is independent of the number and directions of point interferers. Speciﬁcally, we assume that the point interferers are continuously distributed at each direction of the plane but the probability of the interferer occurring at each direction is different in order to calculate the spatial covariance matrix of the point interferers. Moreover, we assume that the noise is additive and uncorrelated with the target signal to obtain the power spectral densities (PSDs) of the target signal and noise. Finally, the proposed post-ﬁlter is calculated using the estimated PSDs. Experimental results prove that the proposed post-ﬁltering algorithm is superior to the comparative algorithms in the scenarios where the number and directions of point interferers are not accurately estimated.
ASR Error Correction with Augmented Transformer for Entity Retrieval

Domain-agnostic Automatic Speech Recognition (ASR) systems suffer from the issue of mistranscribing domain-speciﬁc words, which leads to failures in downstream tasks. In this paper, we present a post-editing ASR error correction method using the Transformer model for entity mention correction and retrieval. Speciﬁcally, we propose a novel augmented variant of the Transformer model that encodes both the word and phoneme sequence of an entity, and attends to phoneme information in addition to word-level information during decoding to correct mistranscribed named entities. We evaluate our method on both the ASR error correction task and the downstream retrieval task. Our method achieves 48.08% entity error rate (EER) reduction in ASR error correction task and 26.74% mean reciprocal rank (MRR) improvement for the retrieval task. In addition, our augmented Transformer model signiﬁcantly outperforms the vanilla Transformer model with 17.89% EER reduction and 1.98% MRR increase, demonstrating the effectiveness of incorporating phoneme information in the correction model.
Fault Attacks In Symmetric Key Cryptosystems

Fault attacks are among the well-studied topics in the area of cryptography. These attacks constitute a powerful tool to recover the secret key used in the encryption process. Fault attacks work by forcing a device to work under non-ideal environmental conditions (such as high temperature) or external disturbances (such as glitch in the power supply) while performing a cryptographic operation. The recent trend shows that the amount of research in this direction; which ranges from attacking a particular primitive, proposing a fault countermeasure, to attacking countermeasures; has grown up substantially and going to stay as an active research interest for a foreseeable future. Hence, it becomes apparent to have a comprehensive yet compact study of the (major) works. This work, which covers a wide spectrum in the present day research on fault attacks that fall under the purview of the symmetric key cryptography, aims at fulﬁlling the absence of an up-to-date survey. We present mostly all aspects of the topic in a way which is not only understandable for a non-expert reader, but also helpful for an expert as a reference.
Harmonic Lowering for Accelerating Harmonic Convolution for Audio Signals

Convolutional neural networks have been successfully applied to a variety of audio signal processing tasks including sound source separation, speech recognition and acoustic scene understanding. Since many pitched sounds have a harmonic structure, an operation, called harmonic convolution, has been proposed to take advantages of the structure appearing in the audio signals. However, the computational cost involved is higher than that of normal convolution. This paper proposes a faster calculation method of harmonic convolution called Harmonic Lowering. The method unrolls the input data to a redundant layout so that the normal convolution operation can be applied. The analysis of the runtimes and the number of multiplication operations show that the proposed method accelerates the harmonic convolution 2 to 7 times faster than the conventional method under realistic parameter settings, while no approximation is introduced.
Medically unexplained symptoms in children: an experimental investigation of the impact of internet searching on parental responses

Abstract Background: Medically unexplained symptoms (MUS) are symptoms for which no medical cause can be identified. For children and adolescents, symptoms can be maintained through parental responses. Aims: The present study investigated the impact that internet searching of symptoms has on parental responses to MUS. Method: One hundred and twenty-seven adult participants read a vignette in which they were asked to imagine they were a parent of a young person with MUS and completed visual analogue scales (VAS) reporting their beliefs, emotions and behavioural intentions about the MUS. Participants were then randomly assigned to one of three conditions: searching reputable websites for further information about the symptoms (n = 47), free search of any websites for further information about the symptoms (n = 38) or a control condition (n = 42) during which participants spent 10 minutes doing their usual behaviour on the internet, for example checking email and social media. Participants then completed the VAS for a second time. Results: Searching reputable websites led to a significantly greater decrease in behaviour VAS scores compared with the free search condition [F (1,123) = 11.374, p < . 001], indicating that participants were less likely to seek a second opinion and to advise the child to avoid usual activities. Conclusions: This study demonstrated that internet searching reputable sites for information regarding physical symptoms can be positive and it may therefore be advisable for health professionals meeting children with MUS to provide the family with information links to reputable sources.
BLSTM-Driven Stream Fusion for Automatic Speech Recognition: Novel Methods and a Multi-Size Window Fusion Example

Optimal fusion of streams for ASR is a nontrivial problem. Recently, so-called posterior-in-posterior-out (PIPO-)BLSTMs have been proposed that serve as state sequence enhancers and have highly attractive training properties. In this work, we adopt the PIPO-BLSTMs and employ them in the context of stream fusion for ASR. Our contributions are the following: First, we show the positive effect of a PIPO-BLSTM as state sequence enhancer for various stream fusion approaches. Second, we conﬁrm the advantageous context-free (CF) training property of the PIPO-BLSTM for all investigated fusion approaches. Third, we show with a fusion example of two streams, stemming from different short-time Fourier transform window lengths, that all investigated fusion approaches take proﬁt. Finally, the turbo fusion approach turns out to be best, employing a CF-type PIPO-BLSTM with a novel iterative augmentation in training.
Attention Wave-U-Net for Acoustic Echo Cancellation

In this paper, a Wave-U-Net based acoustic echo cancellation (AEC) with an attention mechanism is proposed to jointly suppress acoustic echo and background noise. The proposed approach consists of the Wave-U-Net, an auxiliary encoder, and an attention network. In the proposed approach, the Wave-U-Net yields the estimated near-end speech from the mixture, the auxiliary encoder extracts the latent features of the far-end speech, among which the relevant features are provided to the Wave-U-Net by using the attention mechanism. With the attention network, the echo can be effectively suppressed from the mixture. Experimental results on TIMIT dataset show that the proposed approach outperforms the existing methods in terms of the echo return loss enhancement (ERLE) for the single-talk period and the perceptual evaluation of speech quality (PESQ) score for the double-talk period. Furthermore, the robustness of the proposed approach against unseen noise condition is also validated from the experimental results.
UNSW System Description for the Shared Task on Automatic Speech Recognition for Non-Native Children's Speech

In this paper we describe our children’s Automatic Speech Recognition (ASR) system for the first shared task on ASR for English non-native children ’s speech. The acoustic model comprises 6 Convolutional Neural Network (CNN) layers and 12 Factored Time-Delay Neural Network (TDNN-F) layers, trained by data from 5 different children’s speec h corpora. Speed perturbation, Room Impulse Response (RIR), babble noise and non-speech noise data augmentation methods were utilized to enhance the model robustness. Three Language Models (LMs) were employed: an in-domain LM trained on written data and speech transcriptions of non-native children, a LM trained on non-native written data and transcription of both native and non-native children ’s speech and a TEDLIUM LM trained on adult TED talks transcriptions. Lattices produced from the different ASR systems were combined and decoded using the Minimum Bayes-Risk (MBR) decoding algorithm to get the final output. Our system achieved a final Word Error Rate (WER) of 17.55% and 16.59% for both developing and testing sets respectively and ranked second among the 10 teams participating in the task.
Improved Hybrid Streaming ASR with Transformer Language Models

Streaming ASR is gaining momentum due to its wide applicability, though it is still unclear how best to come close to the accuracy of state-of-the-art off-line ASR systems when the output must come within a short delay after the incoming audio stream. Following our previous work on streaming one-pass decoding with hybrid ASR systems and LSTM language models, in this work we report further improvements by replacing LSTMs with Transformer models. First, two key ideas are discussed so as to run these models fast during inference. Then, empirical results on LibriSpeech and TED-LIUM are provided showing that Transformer language models lead to improved recognition rates on both tasks. ASR systems obtained in this work can be seamlessly transfered to a streaming setup with minimal quality losses. Indeed, to the best of our knowledge, no better results have been reported on these tasks when assessed under a streaming setup.
Tweeting During the Covid-19 Pandemic

In this article, we utilize VADER, a rule-based model, to perform sentiment analysis of tweets by President Donald Trump during the early spread of the Covid-19 pandemic across the United States, making it the worst-hit country in the world. We discover a statistically significant negative correlation between the sentiment of his messages and the number of Covid-19 cases in the United States, indicating an effect on the tone of his tweets as the pandemic took its toll on American lives and economy. Furthermore, we also witness a gradual shift from positive to negative sentiment in his messages mentioning China and coronavirus together.
User Concerns & Tradeoffs in Technology-facilitated COVID-19 Response

The COVID-19 pandemic spread across the world in late 2019 and early 2020. As the pandemic spread, technologists joined forces with public health officials to develop apps to support COVID-19 response. Yet, for these technological solutions to benefit public health, users must be willing to adopt these apps. This article details a framework of inputs to a user’s decision to adopt a COVID-19 contact-tracing app or other COVID-19 technology along four major axes: technology benefits, solution accuracy, privacy considerations, and mobile-related costs. This framework is empirically validated via both the literature and a demographically-representative survey of 1,000 Americans.
Non-Parallel Many-to-Many Voice Conversion with PSR-StarGAN

Voice Conversion (VC) aims at modifying source speaker’s speech to sound like that of target speaker while preserving linguistic information of given speech. StarGAN-VC was recently proposed, which utilizes a variant of Generative Adversarial Networks (GAN) to perform non-parallel many-to-many VC. However, the quality of generated speech is not satisfactory enough. An improved method named “PSR-StarGAN-VC” is proposed in this paper by incorporating three improvements. Firstly, perceptual loss functions are introduced to optimize the generator in StarGAN-VC aiming to learn high-level spectral features. Secondly, considering that Switchable Normalization (SN) could learn different operations in different normalization layers of model, it is introduced to replace Batch Normalization (BN) in StarGAN-VC. Lastly, Residual Network (ResNet) is applied to establish the mapping of different layers between the encoder and decoder of generator aiming to retain more semantic features when converting speech, and to reduce the difﬁ-culty of training. Experiment results on the VCC 2018 datasets demonstrate superiority of the proposed method in terms of nat-uralness and speaker similarity.
Shadowability Annotation with Fine Granularity on L2 Utterances and its Improvement with Native Listeners' Script-Shadowing

Language teachers often claim that the goal of speech training should be intelligible enough pronunciations, not native-sounding ones, because some types of accented pronunciations are intelligible or comprehensible enough. However, if one aims to provide a technical framework of automatic assessment based on intelligibility or comprehensibility, s/he has to be faced with a big technical challenge. That is collection of L2 utterances with annotations based on these metrics. Further, learners always want to know which parts (words, morphemes, or syllables) in their speech should be corrected. This means that data collection needs a valid method of intelligibility annotation with ﬁne granularity. In our previous studies, a new metric of shad-owability was introduced, and it was shown experimentally to be highly correlated to perceived intelligibility or comprehensi-bility as well as it was explained theoretically to be potential to give annotations with ﬁne granularity. In this paper, shadowa-bility annotation with ﬁne granularity is examined experimentally, and a new and more valid method of collecting shadowing utterances is introduced. Finally, we tentatively derive frame-based shadowability annotation for L2 utterances.
Effect of Cage and Floor Litter Environment on the Blood Profiles of Broiler Chickens Reared in Derived Savanna Zone of Nigeria

The current study evaluate the effect of cage and floor litter environment on the haematological and serological indices of broiler chickens consisting of 100 Ross and Anak strain of broiler chicks each in a completely randomly design. The blood samples were collected at three intervals of 6, 8 and 10 th week of age. Data were collected on haematological and serological indices of both strains in the two different environments. Haematological indices including Red blood cell (RBC), Packed cell volume (PCV), haemoglobin (Hb), mean corpuscular volume (MCV), mean corpuscular haemoglobin concentration and (erythrocytic values). White blood cells (WBC), heterophils (H), Lymphocytes (L), Heterophils: Lymphocytes (H/L) monocytes (M) Eosinophils (E) and Basophils (B) as Leukocytic indices. Regardless to the strains-cage environment, higher significant (P 0.05) responses were observed for MCHC and monocyte. The serological indices determined were serum total protein (TP) serum albumin (SA), serum globulin (SG), Uric acid (UA) serum glucose (SGL), Creatine (C), Serum aspertate amino transferase, (SAST), total cholesterol (TC) and Serum alanine amino transferase (SALT). Caged birds showed significant (P < 0.05) different values in TP, SA, SG, SALT, SAST and TC than their counterpart on the floor litter environment while higher significant (P < 0.05) values were obtained for creatine and Uric acid. The study concluded higher H:L value which is an indicator of stress in the cage environment and improvement in the floor system could result into better health improvement of birds. The results could be a baseline for breeding programme in this environment.
NEC-TT Speaker Verification System for SRE'19 CTS Challenge

The series of speaker recognition evaluations (SREs) organized by the National Institute of Standards and Technology (NIST) is widely accepted as the de facto benchmark for speaker recognition technology. This paper describes the NEC-TT speaker veriﬁcation system developed for the recent SRE’19 CTS Challenge. Our system is based on an x-vector embedding front-end followed by a thin scoring back-end. We trained a very-deep neural network for x-vector extraction by incorporating residual connections, squeeze-and-excitation networks, and angular-margin softmax at the output layer. We enhanced the back-end with a tandem approach leveraging the beneﬁt of supervised and unsupervised domain adaptation. We obtained over 30% relative reduction in error rate with each of these enhancements at the front-end and back-end, respectively.
Unsupervised Training of Siamese Networks for Speaker Verification

Speaker labeled background data is an essential requirement for most state-of-the-art approaches in speaker recognition, e.g., x-vectors and i-vector/PLDA. However, in reality it is difﬁcult to access large amount of labeled data. In this work, we pro-pose siamese networks for speaker veriﬁcation without using speaker labels. We propose two different siamese networks having two and three branches, respectively, where each branch is a CNN encoder. Since the goal is to avoid speaker labels, we propose to generate the training pairs in an unsupervised manner. The client samples are selected within one database according to highest cosine scores with the anchor in i-vector space. The impostor samples are selected in the same way but from another database. Our double-branch siamese performs binary classiﬁcation using cross entropy loss during training. In testing phase, we obtain speaker veriﬁcation scores directly from its output layer. Whereas, our triple-branch siamese is trained to learn speaker embeddings using triplet loss. During testing, we extract speaker embeddings from its output layer, which are scored in the experiments using cosine scoring. The evaluation is performed on VoxCeleb-1 database, which show that using the proposed unsupervised systems, solely or in fusion, the re-sults get closer to supervised baseline.