Miracle-dz/UniFAR-FTUs huggingface dataset:
The I2R's ASR System for the VOiCES from a Distance Challenge 2019

This paper describes the development of the automatic speech recognition (ASR) system for the submission to the VOiCES from a Distance Challenge 2019. In this challenge, we focused on the fixed condition, where the task is to recognize reverberant and noisy speech based on a limited amount of clean training data. In our system, the mismatch between the training and testing conditions was reduced by using multi-style training where the training data was artificially contaminated with different reverberation and noise sources. Also, the Weighted Prediction Error (WPE) algorithm was used to reduce the reverberant effect in the evaluation data. To boost the system performance, acoustic models of different neural network architectures were trained and the respective systems were fused to give the final output. Moreover, an LSTM language model was used to rescore the lattice to compensate the weak n-gram model trained from only the transcription text. Evaluated on the development set, our system showed an average word error rate (WER) of 27.04%.
DOES SCHWA HAVE AN AUDITORY TARGET? AN ALTERED AUDITORY FEEDBACK STUDY

Schwa is cross-linguistically described as having a variable target (e.g. Koopmans-van Beinum 1994). The present study examines whether speakers are sensitive to auditory feedback when producing schwa. When speakers hear themselves producing a version of their speech where formants have been altered, they will change their motor plans online so that their altered feedback is a better match to the target (e.g. Houde & Jordan 1998). If schwa has no target, then feedback mismatches may not drive a change in production. In this experiment, participants spoke disyllabic iambs and trochees where the auditory feedback of F1 was raised by 100 mels. Both stressed and unstressed syllables showed compensatory decreases in F1, with comparable levels of adaptation in schwa and stressed vowels. However, across different vowels, the magnitude of adaptation in schwa was highly dependent on that of the heterosyllabic vowel, consistent with the idea that schwa is highly assimila-
Isolation, Identification and Antibiotic Susceptibility Profiling of Listeria spp. from Raw Chicken Meat in Durg District of Chhattisgarh, India

Present work was conducted to determine the total aerobic plate count of raw chicken meat samples, isolation of the Listeria spp. and determining their pathogenicity along with antibiotic susceptibility pattern. The 100 raw chicken meat samples, collected from different retail outlets in and around Durg district of Chhattisgarh, revealed mean APC of 23.67×105 cfu/g (6.374 log10 cfu/g). Cultural examination of raw chicken meat samples showed an overall 37% prevalence of Listeria spp. , comprising of L. monocytogenes (16%), L. grayi (11%), L. welshimeri (5%), L. ivanovii (3%) and L. innocua (2%). All the Listeria isolates exhibited a typical β-heamolysis with narrow zone on sheep blood agar and enhancement of hemolytic zone in CAMP test. The haemolytic Listerial isolates developed kerato conjunctivitis in Anton’s test and stunting as well as hemorrhages in liver and heart along with conspicuous thickening of CAM in chicken embryos. Results of antibiotic susceptibility testing of all Listerial isolates further revealed that most of isolates were multidrug resistance to antibiotics. The present work revealed that the raw chicken meat may act as an important source of Listeria for human being. The presence of multiple drug resistance among Listeria spp. isolates provides a evidence of the emergence of multi drug resistant Listeria strains, pointing to an increase in the potential threat to human health.
Hybrid CTC / Attention Architecture for End-to-end Speech Recognition

The hybrid CTC/attention end-to-end automatic speech recognition (ASR) combines CTC ASR system and attention ASR system into a single neural network. Although the hybrid CTC/attention ASR system takes the advantages of both CTC and attention architectures in training and decoding, it remains challenging to be used for streaming speech recognition for its attention mechanism, CTC prefix probability and bidirectional encoder. In this paper, we propose a stable monotonic chunkwise attention (sMoChA) to stream its attention branch and a truncated CTC prefix probability (T-CTC) to stream its CTC branch. On the acoustic model side, we utilize the latencycontrolled bidirectional long short-term memory (LC-BLSTM) to stream its encoder. On the joint CTC/attention decoding side, we propose the dynamic waiting joint decoding (DWDJ) algorithm to collect the decoding hypotheses from the CTC and attention branches. Through the combination of the above methods, we stream the hybrid CTC/attention ASR system without much word error rate degradation.
Towards a Comprehensive Model for Performance Analysis of Opportunistic Routing in Wireless Mesh Networks

Opportunistic Routing (OR) is a promising paradigm that has been proposed for wireless mesh networks. This routing paradigm takes advantage of the broadcast nature of the wireless medium to increase the reliability of transmissions in multi-hop wireless networks. The selection of a set of candidates involves satisfying the basic requirements of the model, in which packets are forwarded toward the destination. In OR, if one of the selected candidates does not receive the packet, another candidate might be able to continue forwarding the packet. The decision of which forwarder to choose is made by coordination between candidates that have successfully received the transmitted packet. In this paper, we propose a Discrete Time Markov Chain as a general model for OR and demonstrate how it can be used to evaluate the performance of OR protocols. We also review three well-known OR protocols that we have selected as a study case. Our study demonstrates how our model facilitates better understanding of the combination of number of candidates and re-transmissions, and their significant contributions to the successful delivery of data packets. Thus, this shows that our model can help the design of future OR protocols, as well as efficient candidate selection algorithms.
Perfect tracking control of discrete-time quadratic TS fuzzy systems via feedback linearisation

ABSTRACT Perfect tracking control is an important and frequently encountered requirement in various industries (e.g. robotic control). We developed a novel systematic framework for designing a fuzzy controller via feedback linearisation to control a class of discrete-time Takagi–Sugeno (TS) fuzzy systems with quadratic rule consequents to achieve such tracking. We established a necessary condition for its local stability and a necessary and sufficient condition for the boundedness of the controller. The feedback linearisation is known to fail to work in certain systems due to the unboundedness of the tracking controller output. To address this issue, we developed a method to check whether any given quadratic TS fuzzy system will cause such a failure. We developed a scheme to ensure that the output of the controller designed for any failure-causing system will be bounded and the resulting controller will attain nearly perfect tracking performance. Applying feedback linearisation to the quadratic fuzzy systems is innovative relative to the literature exclusively dealing with the TS fuzzy systems with linear rule consequents (including our previous results), which are now generalised by the new findings. Two numerical examples are provided to illustrate the effectiveness and utility of our new theoretical results.
THE ECOLOGY OF GEOTHERMAL SPRINGS IN SOUTH-CENTRAL IDAHO

i TABLE OF CONTENTS. ii LIST OF TABLES . iii LIST OF FIGURES . iii LIST OF APPENDICES. ii
Multi-Modal Learning for Speech Emotion Recognition: An Analysis and Comparison of ASR Outputs with Ground Truth Transcription

In this paper we plan to leverage multi-modal learning and automated speech recognition (ASR) systems toward building a speech-only emotion recognition model. Previous studies have shown that emotion recognition models using only acoustic features do not perform satisfactorily in detecting valence level. Text analysis has been shown to be helpful for sentiment classification. We compared classification accuracies obtained from an audio-only model, a text-only model and a multi-modal system leveraging both by performing a cross-validation analysis on IEMOCAP dataset. Confusion matrices show it’s the valence level detection thats being improved by incorporating textual information. In the second stage of experiments, we used two ASR application programming interfaces (APIs) to get the transcriptions. We compare the performances of multi-modal systems using the ASR transcriptions with each other and with that of one using ground truth transcription. We analyze the confusion matrices to determine the effect of using ASR transcriptions instead of ground truth ones on class-wise accuracies. We investigate the generalisability of such a model by performing a cross-corpus study.
Phonetics of modal and breathy nasals in Xitsonga

Xitsonga is a southern Bantu language with a phonation contrast in nasals. This paper reports results from acoustic and articulatory data collected in Thohoyandou, Limpopo. This study is the first to examine Xitsonga breathiness with Electroglottography (EGG). The EGG analyses of 6 Xitsonga speakers show that the contrast varies according to tonal contexts. In the high tone context, modal nasals demonstrated lower open quotient than breathy nasals. In the toneless context, however, the contrast was neutralized. Moreover, female speakers have a breathier voice than male speakers. As in [8], f0 values were consistently lower in breathy tokens.
Theory and Applications of Characteristic Modeling : An Introductory Overview

Characteristic modeling is a control oriented modeling approach that takes into account both the dynamic characteristics of the plant to be controlled and the performance specifications of the control system. In this article, we give an introductory overview of characteristic modeling. The basic concept, key features, and underlying mechanism are first reviewed. The characteristic modeling based control design and the stability of the closed-loop system are then illustrated. An example of successful applications of the characteristic modeling in control designs is recalled to demonstrate the effectiveness of the design method. Finally, motivations of the characteristic modeling are summarized and future work is proposed.
Predicting Speech Intelligibility of Enhanced Speech Using Phone Accuracy of DNN-Based ASR System

The ability of state-of-the-art automatic speech recognition (ASR) systems, which use deep neural networks (DNN), has recently been approaching that of human auditory systems. On the other hand, although measuring the intelligibility of enhanced speech signals is important for developing auditory algorithms and devices, the current measurement methods mainly rely on subjective experiments. Therefore, it would be preferable to employ an ASR system to predict the subjective speech intelligibility (SI) of enhanced speech. In this study, we evaluate the SI prediction performance of DNN-based ASR systems us-ing phone accuracies. We found that an ASR system with multi-condition training achieves the best SI prediction accuracy for enhanced speech when compared with conventional methods (STOI, HASPI) and a recently proposed technique (GEDI). In addition, since our ASR system uses only a phone language model, it offers the advantage of being able to predict intelligibility independently of prior knowledge of words.
TV Viewing and Advertising Targeting Television

Television, the predominant advertisingmedium, is being transformed by the microtargetingcapabilitiesof set-topboxes (STBs). Byprocuring impressionsat the STB level (often denoted “programmatic television”), advertisers can now lower per-exposure costs and/or reach viewers most responsive to advertising creatives. Accordingly, this study uses a proprietary, household-level, singlesource data set to develop an instantaneous show and advertisement viewing model to forecast consumers’ exposure to advertising and the downstream consequences for impressions and sales. Viewing data suggest that personspecific factors dwarf brandor show-specific factors in explaining advertising avoidance, thereby suggesting that device-level advertising targeting can be more effective than existing show-level targeting. Consistent with this observation, the model indicates that microtargeting lowers advertising costs and raises incremental profits considerably relative to show-level targeting. Further, these advantages are amplified when advertisers can buy in real time as opposed to up front.
Blended learning as a resource for integrating self-access and traditional face-to-face tuition in EFL tertiary education

This paper examines the use of blended-learning approaches to carry out self-access processes integrated within conventional face-to-face classroom tuition in EFL university tuition by actively drawing upon elearning. The role of ICTs in resource centres is explored in relation to EFL acquisition. This contribution reports the experience undertaken at the University of Castilla-La Mancha, where a resource centre was used as a complement to traditional instructional processes for students. As well as discussing the type of learning strategies and tutoring needed for the programme, attention is paid to the use of online resources for the learners’ independent-study activity, and the integration of collaborative tools (e.g. e-mail, chats, distribution lists, forums or videoconferencing) to construct interaction among self-students and counsellors, and promote autonomy. The discussion of results considers the reluctance of traditional institutions to implement blended-learning programmes and the positive effects of curricular adaptations based on blended learning approaches.
Efficient Evaluation of Sparse Jacobians by Matrix Compression , Part I : Motivation and Theory

The accurate and efficient calculations of Jacobians matrices at a sequence of arguments is a key ingredient of numerical methods for nonlinear problems in scientific computing. It has been known since the seminal work of Curtis Powell and Reid [2] that once their sparsity pattern is known Jacobians can be estimated on the basis of divided differences for a set carefully chosen directions. The number p of such seed directions and thus extra function evaluations can often be limited a priori to a smallish number, which is typically much smaller than the number of independent variables and unaffected by grid sizes and other discretization parameters. The cost factor p is bounded below by the maximal number of nonzeros per row, which is actually sufficient for Jacobian estimation using Newsam-Ramsdell compression. This NR approach is numerically less stable than the CPR method, which was therefore preferred in practice as divided differences are strongly affected by truncation and round off errors. However now, using automatic or algorithmic differentiation, one obtains directional derivatives with working accuracy and can thus utilize the more economical NR approach. Key–Words: Sparse Matrices, Jacobians, Hessians, Algorithmic Differentiation, Polynomial Interpolation
A Non-Causal FFTNet Architecture for Speech Enhancement

In this paper, we suggest a new parallel, non-causal and shallow waveform domain architecture for speech enhancement based on FFTNet, a neural network for generating high quality audio waveform. In contrast to other waveform based approaches like WaveNet, FFTNet uses an initial wide dilation pattern. Such an architecture better represents the long term correlated structure of speech in the time domain, where noise is usually highly non-correlated, and therefore it is suitable for waveform domain based speech enhancement. To further strengthen this feature of FFTNet, we suggest a non-causal FFTNet architecture, where the present sample in each layer is estimated from the past and future samples of the previous layer. By suggesting a shallow network and applying non-causality within certain limits, the suggested FFTNet for speech enhancement (SE-FFTNet) uses much fewer parameters compared to other neural network based approaches for speech enhancement like WaveNet and SEGAN. Specifically, the suggested network has considerably reduced model parameters: 32% fewer compared to WaveNet and 87% fewer compared to SEGAN. Finally, based on subjective and objective metrics, SE-FFTNet outperforms WaveNet in terms of enhanced signal quality, while it provides equally good performance as SEGAN. A Tensorflow implementation of the architecture is provided at 1 .
The Use of Thermochromic Liquid Crystals to Investigate Heat Transfer Enhancement in a Channel with a Protrusion

Thermochromic liquid crystals (TLCs) were used to investigate the effectiveness of a single protrusion (a hemisphere of 10mm diameter) for enhancement of heat transfer in a 10mm x 40mm cross-section channel filled with warm water flowing at Reynolds numbers of approximately 570 and 1000. The channel was separated from an adjacent channel of colder water by an aluminium plate. Two methods were used in order to gain the maximum possible information for comparison to numerical results: a crystal-coated sheet applied to the surface of the channel featuring the protrusion, and suspended crystals illuminated by a sheet of light to create a “slice” down the centreline of the channel. The on-surface technique yielded useful data which compared extremely well to CFD, and the suspended crystals provided an additional level of insight, though their use in such a small channel with only a simple light sheet and optics produced sub-optimal results. Overall, the techniques applied show promise, but are most effective when combined with additional information gained from other experimental techniques and numerical modelling to provide the greatest level of knowledge of the flowfield.
Development of a High-Performance Magnetic Field Sensor and Its Application to a Magnetic Field Visualization System Using the Augmented Reality Technique

Two- or three-dimensional visualization of magnetic flux density distribution around sources is quite informative for understanding the field environment intuitively. Measurement of magnetic flux density, as well as positioning of measuring points in space, has to be performed in a short time because temporal variation in the source current results in different field environments. It is also valuable to obtain the frequency spectrum of the magnetic field simultaneously at the time of field measurement, especially for sources generating high-frequency components like inverter-driven equipment. This paper develops a high-performance magnetic field sensor which satisfies the above requirements. And, as an application of it, the magnetic field visualization system using the augmented reality (AR) technique is proposed by combining two sensors: one is a magnetic field sensor described above, and the other is a Kinect sensor which has a skeleton tracking function as position determination.
The interrelationship between initial profits , growth-focused firms and profit-focused firms : Evidence from KSE 100-index Pakistan Pakistan

Profit and growth are considered as important goals in managing firms. This study tests the impact of initial profits on the growth and profit on a sample of firms listed at Karachi Stock Exchange (KSE) for the period 2006 to 2011. In addition to initial profits the study also checked the impact of leverage, firm’s size and age on profitable growth. Logistic regression borrowed from the work of Davidsson et al. (2009) was used to find out whether profit at initial stages of the firm determines profitable growth for the firm. The average growth rate of the companies was calculated for the last three years. After calculating the average growth rate the firms were then classified into two groups based on the median value of the growth. High growth firms were further classified into low and high profits based on the median of ROA (Return on Assets). The findings showed that initial profits had a positive significant impact on profitable growth. While firm’s size, age and leverage had no impact on profitable growth of the sampled firms. In comparison to growth-focused firms, profit-focused firms are more likely to reach a state of high growth and high profits.
The Effect of Childcare Costs on Mothers ’ Labor Force Participation *

Using micro data from the Basic Survey on People’s Life for 1998, this paper investigates (1) the childcare situation of preschoolers and (2) the economic situation of the households using licensed day-care centers. Then it analyzes (3) the impact of nursery fees on the labor force participation of mothers with preschool children. Our main findings are as follows. First, households using licensed day-care centers are not always low-income households. Considering the fact that a large amount of subsidies is granted to licensed day-care centers, the question of fairness arises, because there are households that take care of children at home. Second, in most cases, mothers who use licensed day-care centers earn less than 1.3 million yen a year, so that they pay neither taxes nor social security premiums. Third, nursery fees have significantly negative effects on the labor force participation of mothers, and its elasticity is about -0.60. We also find that raising subsidies on nursery fees is effective in increasing the employment of mothers, especially that of low-income groups.
1 Spatial Gap-Filling of ESA CCI Satellite-Derived Soil 2 Moisture based on Linear Geostatistics 3

Soil moisture plays a key role in the Earth’s water and carbon cycles, but acquisition of 10 continuous (i.e., gap-free) soil moisture measurements across large regions is a challenging task due 11 to limitations of currently available point measurements. Satellites offer critical information for soil 12 moisture over large areas on a regular basis (e.g., ESA CCI, NASA SMAP), however, there are 13 regions where satellite-derived soil moisture cannot be estimated because of certain circumstances 14 such as high canopy density, frozen soil, or extreme dry conditions. We compared and tested two 15 approaches--Ordinary Kriging (OK) interpolation and General Linear Models (GLM)--to model soil 16 moisture and fill spatial data gaps from the European Space Agency Climate Change Initiative (ESA 17 CCI) version 3.2 (and compared them with version 4.4) from January 2000 to September 2012, over 18 a region of 465,777 km2 across the Midwest of the USA. We tested our proposed methods to fill gaps 19 in the original ESA CCI product, and two data subsets, removing 25% and 50% of the initially 20 available valid pixels. We found a significant correlation coefficient (r = 0.523, RMSE = 0.092 m3m-3) 21 between the original satellite-derived soil moisture product with ground-truth data from the North 22 American Soil Moisture Database (NASMD). Predicted soil moisture using OK also had significant 23 correlation coefficients with NASMD data, when using 100% (r = 0.522, RMSE = 0.092 m3m-3), 75% 24 (r = 0.526, RMSE = 0.092 m3m-3) and 50% (r = 0.53, RMSE = 0.092 m3m-3) of available valid pixels for 25 each month of the study period. GLM had lower but significant correlation coefficients with 26 NASMD data (average r = 0.478, RMSE = 0.092 m3m-3) when using the same subsets of available data 27 (i.e., 100%, 75%, 50%). Our results provide support for OK as a technique to gap-fill spatial missing 28 values of satellite-derived soil moisture products across the Midwest of the USA. 29