pwc-archive/papers-with-abstracts huggingface dataset:
Fiscal stimulus as an optimal control problem
Authors: Philip A. Ernst, Michael B. Imerman, Larry Shepp, Quan Zhou
Date: 2014-10-22 00:00:00

During the Great Recession, Democrats in the United States argued that government spending could be utilized to "grease the wheels" of the economy in order to create wealth and to increase employment; Republicans, on the other hand, contended that government spending is wasteful and discouraged investment, thereby increasing unemployment. Today, in 2020, we find ourselves in the midst of another crisis where government spending and fiscal stimulus is again being considered as a solution. In the present paper, we address this question by formulating an optimal control problem generalizing the model of Radner & Shepp (1996). The model allows for the company to borrow continuously from the government. We prove that there exists an optimal strategy; rigorous verification proofs for its optimality are provided. We proceed to prove that government loans increase the expected net value of a company. We also examine the consequences of different profit-taking behaviors among firms who receive fiscal stimulus.
Equity Impacts of Dollar Store Vaccine Distribution
Authors: Judith A. Chevalier, Jason L. Schwartz, Yihua Su, Kevin R. Williams
Date: 2021-04-03 00:00:00

We use geospatial data to examine the unprecedented national program currentlyunderway in the United States to distribute and administer vaccines against COVID-19. We quantify the impact of the proposed federal partnership with the companyDollar General to serve as vaccination sites and compare vaccine access with DollarGeneral to the current Federal Retail Pharmacy Partnership Program. Although dollarstores have been viewed with skepticism and controversy in the policy sector, we showthat, relative to the locations of the current federal program, Dollar General stores aredisproportionately likely to be located in Census tracts with high social vulnerability;using these stores as vaccination sites would greatly decrease the distance to vaccinesfor both low-income and minority households. We consider a hypothetical alternativepartnership with Dollar Tree and show that adding these stores to the vaccinationprogram would be similarly valuable, but impact different geographic areas than theDollar General partnership. Adding Dollar General to the current pharmacy partnersgreatly surpasses the goal set by the Biden administration of having 90% of the popu-lation within 5 miles of a vaccine site. We discuss the potential benefits of leveragingthese partnerships for other vaccinations, including against influenza.
Occupational Mobility: Theory and Estimation for Italy
Authors: Irene Brunetti, Davide Fiaschi
Date: 2021-04-03 00:00:00

This paper presents a model where intergenerational occupational mobility is the joint outcome of three main determinants: income incentives, equality of opportunity and changes in the composition of occupations. The model rationalizes the use of transition matrices to measure mobility, which allows for the identification of asymmetric mobility patterns and for the formulation of a specific mobility index for each determinant. Italian children born in 1940-1951 had a lower mobility with respect to those born after 1965. The steady mobility for children born after 1965, however, covers a lower structural mobility in favour of upper-middle classes and a higher downward mobility from upper-middle classes. Equality of opportunity was far from the perfection but steady for those born after 1965. Changes in income incentives instead played a major role, leading to a higher downward mobility from upper-middle classes and lower upward mobility from the lower class.
Screening for breakthroughs: Omitted proofs
Authors: Gregorio Curello, Ludvig Sinander
Date: 2021-04-05 00:00:00

This document contains all proofs omitted from our working paper 'Screening for breakthroughs'; specifically, the March 2025 version of the paper (arXiv:2011.10090v9).
Speaker conditioned acoustic modeling for multi-speaker conversational ASR
Authors: Srikanth Raj Chetupalli, Sriram Ganapathy
Date: 2021-04-05 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speaker-diarization, Speaker Diarization, speech-recognition, Speech Recognition

In this paper, we propose a novel approach for the transcription of speech conversations with natural speaker overlap, from single channel speech recordings. The proposed model is a combination of a speaker diarization system and a hybrid automatic speech recognition (ASR) system. The speaker conditioned acoustic model (SCAM) in the ASR system consists of a series of embedding layers which use the speaker activity inputs from the diarization system to derive speaker specific embeddings. The output of the SCAM are speaker specific senones that are used for decoding the transcripts for each speaker in the conversation. In this work, we experiment with the automatic speaker activity decisions generated using an end-to-end speaker diarization system. A joint learning approach is also proposed where the diarization model and the ASR acoustic model are jointly optimized. The experiments are performed on the mixed-channel two speaker recordings from the Switchboard corpus of telephone conversations. In these experiments, we show that the proposed acoustic model, incorporating speaker activity decisions and joint optimization, improves significantly over the ASR system with explicit source filtering (relative improvements of 12% in word error rate (WER) over the baseline system).
The Multi-speaker Multi-style Voice Cloning Challenge 2021
Authors: Qicong Xie, Xiaohai Tian, Guanghou Liu, Kun Song, Lei Xie, Zhiyong Wu, Hai Li, Song Shi, Haizhou Li, Fen Hong, Hui Bu, Xin Xu
Date: 2021-04-05 00:00:00
Tasks: Benchmarking, Voice Cloning

The Multi-speaker Multi-style Voice Cloning Challenge (M2VoC) aims to provide a common sizable dataset as well as a fair testbed for the benchmarking of the popular voice cloning task. Specifically, we formulate the challenge to adapt an average TTS model to the stylistic target voice with limited data from target speaker, evaluated by speaker identity and style similarity. The challenge consists of two tracks, namely few-shot track and one-shot track, where the participants are required to clone multiple target voices with 100 and 5 samples respectively. There are also two sub-tracks in each track. For sub-track a, to fairly compare different strategies, the participants are allowed to use only the training data provided by the organizer strictly. For sub-track b, the participants are allowed to use any data publicly available. In this paper, we present a detailed explanation on the tasks and data used in the challenge, followed by a summary of submitted systems and evaluation results.
Citrinet: Closing the Gap between Non-Autoregressive and Autoregressive End-to-End Models for Automatic Speech Recognition
Authors: Somshubra Majumdar, Jagadeesh Balam, Oleksii Hrinchuk, Vitaly Lavrukhin, Vahid Noroozi, Boris Ginsburg
Date: 2021-04-05 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

We propose Citrinet - a new end-to-end convolutional Connectionist Temporal Classification (CTC) based automatic speech recognition (ASR) model. Citrinet is deep residual neural model which uses 1D time-channel separable convolutions combined with sub-word encoding and squeeze-and-excitation. The resulting architecture significantly reduces the gap between non-autoregressive and sequence-to-sequence and transducer models. We evaluate Citrinet on LibriSpeech, TED-LIUM2, AISHELL-1 and Multilingual LibriSpeech (MLS) English speech datasets. Citrinet accuracy on these datasets is close to the best autoregressive Transducer models.
Speaker Recognition Based on Deep Learning: An Overview
Authors: Zhongxin Bai, Xiao-Lei Zhang
Date: 2020-12-02 00:00:00
Tasks: Deep Learning, Domain Adaptation, speaker-diarization, Speaker Diarization, Speaker Recognition, Speaker Verification, Speech Enhancement

Speaker recognition is a task of identifying persons from their voices. Recently, deep learning has dramatically revolutionized speaker recognition. However, there is lack of comprehensive reviews on the exciting progress. In this paper, we review several major subtasks of speaker recognition, including speaker verification, identification, diarization, and robust speaker recognition, with a focus on deep-learning-based methods. Because the major advantage of deep learning over conventional methods is its representation ability, which is able to produce highly abstract embedding features from utterances, we first pay close attention to deep-learning-based speaker feature extraction, including the inputs, network structures, temporal pooling strategies, and objective functions respectively, which are the fundamental components of many speaker recognition subtasks. Then, we make an overview of speaker diarization, with an emphasis of recent supervised, end-to-end, and online diarization. Finally, we survey robust speaker recognition from the perspectives of domain adaptation and speech enhancement, which are two major approaches of dealing with domain mismatch and noise problems. Popular and recently released corpora are listed at the end of the paper.
Hi-Fi Multi-Speaker English TTS Dataset
Authors: Evelina Bakhturina, Vitaly Lavrukhin, Boris Ginsburg, Yang Zhang
Date: 2021-04-03 00:00:00
Tasks: text-to-speech, Text to Speech

This paper introduces a new multi-speaker English dataset for training text-to-speech models. The dataset is based on LibriVox audiobooks and Project Gutenberg texts, both in the public domain. The new dataset contains about 292 hours of speech from 10 speakers with at least 17 hours per speaker sampled at 44.1 kHz. To select speech samples with high quality, we considered audio recordings with a signal bandwidth of at least 13 kHz and a signal-to-noise ratio (SNR) of at least 32 dB. The dataset is publicly released at http://www.openslr.org/109/ .
Adversarial Joint Training with Self-Attention Mechanism for Robust End-to-End Speech Recognition
Authors: Lujun Li, Yikai Kang, Yuchen Shi, Ludwig Kürzinger, Tobias Watzel, Gerhard Rigoll
Date: 2021-04-03 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Speech Enhancement, speech-recognition, Speech Recognition

Lately, the self-attention mechanism has marked a new milestone in the field of automatic speech recognition (ASR). Nevertheless, its performance is susceptible to environmental intrusions as the system predicts the next output symbol depending on the full input sequence and the previous predictions. Inspired by the extensive applications of the generative adversarial networks (GANs) in speech enhancement and ASR tasks, we propose an adversarial joint training framework with the self-attention mechanism to boost the noise robustness of the ASR system. Generally, it consists of a self-attention speech enhancement GAN and a self-attention end-to-end ASR model. There are two highlights which are worth noting in this proposed framework. One is that it benefits from the advancement of both self-attention mechanism and GANs; while the other is that the discriminator of GAN plays the role of the global discriminant network in the stage of the adversarial joint training, which guides the enhancement front-end to capture more compatible structures for the subsequent ASR module and thereby offsets the limitation of the separate training and handcrafted loss functions. With the adversarial joint optimization, the proposed framework is expected to learn more robust representations suitable for the ASR task. We execute systematic experiments on the corpus AISHELL-1, and the experimental results show that on the artificial noisy test set, the proposed framework achieves the relative improvements of 66% compared to the ASR model trained by clean data solely, 35.1% compared to the speech enhancement & ASR scheme without joint training, and 5.3% compared to multi-condition training.
ECAPA-TDNN Embeddings for Speaker Diarization
Authors: Nauman Dawalatabad, Mirco Ravanelli, François Grondin, Jenthe Thienpondt, Brecht Desplanques, Hwidong Na
Date: 2021-04-03 00:00:00
Tasks: speaker-diarization, Speaker Diarization, Speaker Verification

Learning robust speaker embeddings is a crucial step in speaker diarization. Deep neural networks can accurately capture speaker discriminative characteristics and popular deep embeddings such as x-vectors are nowadays a fundamental component of modern diarization systems. Recently, some improvements over the standard TDNN architecture used for x-vectors have been proposed. The ECAPA-TDNN model, for instance, has shown impressive performance in the speaker verification domain, thanks to a carefully designed neural model. In this work, we extend, for the first time, the use of the ECAPA-TDNN model to speaker diarization. Moreover, we improved its robustness with a powerful augmentation scheme that concatenates several contaminated versions of the same signal within the same training batch. The ECAPA-TDNN model turned out to provide robust speaker embeddings under both close-talking and distant-talking conditions. Our results on the popular AMI meeting corpus show that our system significantly outperforms recently proposed approaches.
Deep Feature CycleGANs: Speaker Identity Preserving Non-parallel Microphone-Telephone Domain Adaptation for Speaker Verification
Authors: Saurabh Kataria, Jesús Villalba, Piotr Żelasko, Laureano Moro-Velázquez, Najim Dehak
Date: 2021-04-03 00:00:00
Tasks: Domain Adaptation, Speaker Verification, Translation

With the increase in the availability of speech from varied domains, it is imperative to use such out-of-domain data to improve existing speech systems. Domain adaptation is a prominent pre-processing approach for this. We investigate it for adapt microphone speech to the telephone domain. Specifically, we explore CycleGAN-based unpaired translation of microphone data to improve the x-vector/speaker embedding network for Telephony Speaker Verification. We first demonstrate the efficacy of this on real challenging data and then, to improve further, we modify the CycleGAN formulation to make the adaptation task-specific. We modify CycleGAN's identity loss, cycle-consistency loss, and adversarial loss to operate in the deep feature space. Deep features of a signal are extracted from an auxiliary (speaker embedding) network and, hence, preserves speaker identity. Our 3D convolution-based Deep Feature Discriminators (DFD) show relative improvements of 5-10% in terms of equal error rate. To dive deeper, we study a challenging scenario of pooling (adapted) microphone and telephone data with data augmentations and telephone codecs. Finally, we highlight the sensitivity of CycleGAN hyper-parameters and introduce a parameter called probability of adaptation.
A Lossless Intra Reference Block Recompression Scheme for Bandwidth Reduction in HEVC-IBC
Authors: Jiyuan Hu, Jun Wang, Guangyu Zhong, Jian Cao, Ren Mao, Fan Liang
Date: 2021-04-05 00:00:00
Tasks: Prediction, Texture Classification

The reference frame memory accesses in inter prediction result in high DRAM bandwidth requirement and power consumption. This problem is more intensive by the adoption of intra block copy (IBC), a new coding tool in the screen content coding (SCC) extension to High Efficiency Video Coding (HEVC). In this paper, we propose a lossless recompression scheme that compresses the reference blocks in intra prediction, i.e., intra block copy, before storing them into DRAM to alleviate this problem. The proposal performs pixel-wise texture analysis with an edge-based adaptive prediction method yet no signaling for direction in bitstreams, thus achieves a high gain for compression. Experimental results demonstrate that the proposed scheme shows a 72% data reduction rate on average, which solves the memory bandwidth problem.
Unsupervised Classification for Polarimetric SAR Data Using Variational Bayesian Wishart Mixture Model with Inverse Gamma-Gamma Prior
Authors: Shijie Ren, Feng Zhou, Changlong Wang
Date: 2021-04-04 00:00:00
Tasks: Clustering, Image Clustering

Although various clustering methods have been successfully applied to polarimetric synthetic aperture radar (PolSAR) image clustering tasks, most of the available approaches fail to realize automatic determination of cluster number, nor have they derived an exact distribution for the number of looks. To overcome these limitations and achieve robust unsupervised classification of PolSAR images, this paper proposes the variational Bayesian Wishart mixture model (VBWMM), where variational Bayesian expectation maximization (VBEM) technique is applied to estimate the variational posterior distribution of model parameters iteratively. Besides, covariance matrix similarity and geometric similarity are combined to incorporate spatial information of PolSAR images. Furthermore, we derive a new distribution named inverse gamma-gamma (IGG) prior that originates from the log-likelihood function of proposed model to enable efficient handling of number of looks. As a result, we obtain a closed-form variational lower bound, which can be used to evaluate the convergence of proposed model. We validate the superiority of proposed method in clustering performance on four real-measured datasets and demonstrate significant improvements towards conventional methods. As a by-product, the experiments show that our proposed IGG prior is effective in estimating the number of looks.
Multi-Feature Semi-Supervised Learning for COVID-19 Diagnosis from Chest X-ray Images
Authors: Xiao Qi, John L. Nosher, David J. Foran, Ilker Hacihaliloglu
Date: 2021-04-04 00:00:00
Tasks: Computed Tomography (CT), COVID-19 Diagnosis, Decision Making, Management

Computed tomography (CT) and chest X-ray (CXR) have been the two dominant imaging modalities deployed for improved management of Coronavirus disease 2019 (COVID-19). Due to faster imaging, less radiation exposure, and being cost-effective CXR is preferred over CT. However, the interpretation of CXR images, compared to CT, is more challenging due to low image resolution and COVID-19 image features being similar to regular pneumonia. Computer-aided diagnosis via deep learning has been investigated to help mitigate these problems and help clinicians during the decision-making process. The requirement for a large amount of labeled data is one of the major problems of deep learning methods when deployed in the medical domain. To provide a solution to this, in this work, we propose a semi-supervised learning (SSL) approach using minimal data for training. We integrate local-phase CXR image features into a multi-feature convolutional neural network architecture where the training of SSL method is obtained with a teacher/student paradigm. Quantitative evaluation is performed on 8,851 normal (healthy), 6,045 pneumonia, and 3,795 COVID-19 CXR scans. By only using 7.06% labeled and 16.48% unlabeled data for training, 5.53% for validation, our method achieves 93.61\% mean accuracy on a large-scale (70.93%) test data. We provide comparison results against fully supervised and SSL methods. Code: https://github.com/endiqq/Multi-Feature-Semi-Supervised-Learning-for-COVID-19-CXR-Images
BlinQS: Blind Quality Scalable Image Compression Algorithm without using PCRD Optimization
Authors: Naveen Cheggoju, Vishal R. Satpute
Date: 2021-04-03 00:00:00
Tasks: Image Compression

Quality Scalability is one of the important features of interactive imaging to obtain better perceptual quality at a specified target bit rate. In JPEG 2000, it is achieved using quality layers obtained by Rate-Distortion (R-D) optimization techniques in Tier-II coding. Some important concerns here are: (i) inefficient conventional Post-Compression Rate-Distortion (PCRD) optimization algorithms, (ii) lack of quality scalability for less or single quality layer string. This paper takes the above mentioned concerns into account and proposes a Blind Quality Scalable (BlinQS) algorithm that provides scalability with the least computational complexity. The novel part of this method is to eliminate the Tier-II coding and add a blind string selection algorithm through a normal distribution for efficient rate control. The results obtained suggest that the proposed method achieves better results than JPEG-2000 at single quality layer and achieves results close to JPEG-2000 without using PCRD optimization algorithms.
Hybrid Relay-Reflecting Intelligent Surface-Aided Wireless Communications: Opportunities, Challenges, and Future Perspectives
Authors: Nhan Thanh Nguyen, Jiguang He, Van-Dinh Nguyen, Henk Wymeersch, Derrick Wing Kwan Ng, Robert Schober, Symeon Chatzinotas, Markku Juntti
Date: 2021-04-05 00:00:00

Reconfigurable intelligent surfaces (RISs) have emerged as a cost- and energy-efficient technology that can customize and program the physical propagation environment by reflecting radio waves in preferred directions. However, the purely passive reflection of RISs not only limits the end-to-end channel beamforming gains, but also hinders the acquisition of accurate channel state information for the phase control at RISs. In this paper, we provide an overview of a hybrid relay-reflecting intelligent surface (HR-RIS) architecture, in which only a few elements are active and connected to power amplifiers and radio frequency chains. The introduction of a small number of active elements enables a remarkable system performance improvement which can also compensate for losses due to hardware impairments such as the deployment of limited-resolution phase shifters. Particularly, the active processing facilitates efficient channel estimation and localization at HR-RISs. We present two practical architectures for HR-RISs, namely, fixed and dynamic HR-RISs, and discuss their applications to beamforming, channel estimation, and localization. The benefits, key challenges, and future research directions for HR-RIS-aided communications are also highlighted. Numerical results for an exemplary deployment scenario show that HR-RISs with only four active elements can attain up to 42.8 percent and 41.8 percent improvement in spectral efficiency and energy efficiency, respectively, compared with conventional RISs.
Cross-Validated Tuning of Shrinkage Factors for MVDR Beamforming Based on Regularized Covariance Matrix Estimation
Authors: Lei Xie, Zishu He, Jun Tong, Jun Li, Jiangtao Xi
Date: 2021-04-05 00:00:00

This paper considers the regularized estimation of covariance matrices (CM) of high-dimensional (compound) Gaussian data for minimum variance distortionless response (MVDR) beamforming. Linear shrinkage is applied to improve the accuracy and condition number of the CM estimate for low-sample-support cases. We focus on data-driven techniques that automatically choose the linear shrinkage factors for shrinkage sample covariance matrix ($\text{S}^2$CM) and shrinkage Tyler's estimator (STE) by exploiting cross validation (CV). We propose leave-one-out cross-validation (LOOCV) choices for the shrinkage factors to optimize the beamforming performance, referred to as $\text{S}^2$CM-CV and STE-CV. The (weighted) out-of-sample output power of the beamfomer is chosen as a proxy of the beamformer performance and concise expressions of the LOOCV cost function are derived to allow fast optimization. For the large system regime, asymptotic approximations of the LOOCV cost functions are derived, yielding the $\text{S}^2$CM-AE and STE-AE. In general, the proposed algorithms are able to achieve near-oracle performance in choosing the linear shrinkage factors for MVDR beamforming. Simulation results are provided for validating the proposed methods.