pwc-archive/papers-with-abstracts huggingface dataset:
Modeling the Feedback of AI Price Estimations on Actual Market Values
Authors: Viorel Silaghi, Zobaida Alssadi, Ben Mathew, Majed Alotaibi, Ali Alqarni, Marius Silaghi
Date: 2024-03-13 00:00:00

Public availability of Artificial Intelligence generated information can change the markets forever, and its factoring into economical dynamics may take economists by surprise, out-dating models and schools of thought. Real estate hyper-inflation is not a new phenomenon but its consistent and almost monotonous persistence over 12 years, coinciding with prominence of public estimation information from Zillow, a successful Mass Real Estate Estimator (MREE), could not escape unobserved. What we model is a repetitive theoretical game between the MREE and the home owners, where each player has secret information and expertise. If the intention is to keep housing affordable and maintain old American lifestyle with broad home-ownership, new challenges are defined. Simulations show that a simple restriction of MREE-style price estimation availability to opt-in properties may help partially reduce feedback loop by acting on its likely causes, as suggested by experimental simulation models. The conjecture that the MREE pressure on real estate inflation rate is correlated with the absolute MREE estimation errors, which is logically explainable, is then validated in simulations.
Organizational transformation: The impact of servant leadership on work ethic culture with burnout as a mediating factor in the hospitality industry
Authors: Darul Wiyono, Rinaldi Tanjung, Hedi Setiadi, Sri Marini, Yayan Sugiarto
Date: 2024-02-27 00:00:00

Orientation: The study explores the connections among servant leadership, burnout, and work ethic culture in organizations. It aims to provide a detailed understanding of how servant leadership influences work ethic culture, especially by considering the role of burnout. Research Purpose: This study aims to understand how servant leadership influences work ethic culture and explore the mediating role of burnout in this relationship. Motivation for the Study: This study wants to fill gaps in our understanding of how servant leadership, burnout, and work ethic culture are connected. It seeks to add useful insights to what we already know from previous research. Research Approach/Design and Method: The study, using surveys and statistics, examines the links between servant leadership, burnout, and work ethic culture in 113 hotels in Bandung, Indonesia, with 339 participants. A 183-sample, chosen with a 0.05 margin of error, underwent SEM-PLS analysis using SmartPLS 3.0. Main Findings: The key findings underscore that servant leadership exerts a positive influence on work ethic culture, and burnout plays a pivotal mediating role in this dynamic. The results shed light on the intricate dynamics shaping organizational cultures. Practical/Managerial Implications: The findings aid organizations in forming supportive leadership policies, promoting employee well-being, and fostering ethical work culture. Managers can apply these insights to enhance leadership practices and reduce burnout impact. Contribution/Value-Add: This study clarifies the connection between servant leadership, burnout, and work ethic culture. The findings offer insights for future research and practical actions in organizational leadership.
Information Greenhouse: Optimal Persuasion for Medical Test-Avoiders
Authors: Zhuo Chen
Date: 2024-07-03 00:00:00

Patients often delay or reject medical tests due to information avoidance, which hinders timely reception of necessary treatments. This paper studies the optimal information policy to persuade an information-avoidant patient to undergo the test and make the best choice that maximizes his health. The patient sequentially decides whether to take the test and the optimal treatment plan. The information provided is about the background knowledge of the disease, and disclosure can take place both before and after the test decision. The optimal information policy depends on whether the patient is willing to be tested when he is completely pessimistic. If so, the optimal policy features warning-in-advance: the disclosure only takes place before the test, and the bad news guarantees the patient to be tested and be treated even without further information. If not, the optimal policy constructs an information greenhouse: an information structure that provides high anticipatory utility is committed when the patient is tested and the test result is bad. Extensions to ex ante participation constraint and general information preference are also considered.
Comparative Patience
Authors: Mark Whitmeyer
Date: 2024-07-02 00:00:00

We begin by formulating and characterizing a dominance criterion for prize sequences: $x$ dominates $y$ if any impatient agent prefers $x$ to $y$. With this in hand, we define a notion of comparative patience. Alice is more patient than Bob if Alice's normalized discounted utility gain by going from any $y$ to any dominating $x$ is less than Bob's discounted utility gain from such an improvement. We provide a full characterization of this relation in terms of the agents' discount rules.
Random Attention and Unobserved Reference Alternatives
Authors: Varun Bansal
Date: 2024-07-01 00:00:00

In this paper, I develop and characterize a random attention model with unobserved reference alternatives. The decision-maker pays attention to different subsets of the available set of alternatives randomly. The reference alternatives are exactly those alternatives that are always paid attention to, i.e. they are attention-privileged. These alternatives are unknown to the outside observer. The characterization allows for a complete identification of the reference alternatives and a coarse identification of the underlying preferences. I then restrict the model by considering the independent random attention function and provide a complete identification of the underlying preferences.
Nash equilibria of games with generalized complementarities
Authors: Lu Yu
Date: 2024-06-30 00:00:00

To generalize complementarities for games, we introduce some conditions weaker than quasisupermodularity and the single crossing property. We prove that the Nash equilibria of a game satisfying these conditions form a nonempty complete lattice. This is a purely order-theoretic generalization of Zhou's theorem.
Counterexamples to "Transitive Regret"
Authors: Yuan Chang, Shuo Li Liu
Date: 2024-06-14 00:00:00

Theorem 1 in Bikhchandani & Segal (2011; Theoretical Economics) suggests that a complete, transitive, monotonic, and continuous preference is regret based if and only if it is expected utility. Their Proposition 1 suggests that transitivity and continuity of a regret-based preference implies an equivalence condition: if random variables $X$ and $Y$ have the same distribution, then $X\sim Y$. We give counterexamples to Proposition 1.
Information About Other Players in Mechanism Design
Authors: Eric Yan
Date: 2024-05-28 00:00:00

We study mechanism design settings where the planner has an interest in agents receiving noisy signals about the types of other agents. We show that additional information about other agents can eliminate undesired equilibria, making it helpful to a planner interested in full implementation, designing a mechanism for which every equilibrium outcome is desirable. We provide a sufficient condition under which a social choice function that is not fully implementable when agents have no information about types of other agents can become fully implementable when agents have additional information.
SA-WavLM: Speaker-Aware Self-Supervised Pre-training for Mixture Speech
Authors: Jingru Lin, Meng Ge, Junyi Ao, Liqun Deng, Haizhou Li
Date: 2024-07-03 00:00:00
Tasks: Self-Supervised Learning

It was shown that pre-trained models with self-supervised learning (SSL) techniques are effective in various downstream speech tasks. However, most such models are trained on single-speaker speech data, limiting their effectiveness in mixture speech. This motivates us to explore pre-training on mixture speech. This work presents SA-WavLM, a novel pre-trained model for mixture speech. Specifically, SA-WavLM follows an "extract-merge-predict" pipeline in which the representations of each speaker in the input mixture are first extracted individually and then merged before the final prediction. In this pipeline, SA-WavLM performs speaker-informed extractions with the consideration of the interactions between different speakers. Furthermore, a speaker shuffling strategy is proposed to enhance the robustness towards the speaker absence. Experiments show that SA-WavLM either matches or improves upon the state-of-the-art pre-trained models.
Zero-Bit Transmission of Adaptive Pre- and De-emphasis Filters for Speech and Audio Coding
Authors: Niloofar Omidi Piralideh, Philippe Gournay, Roch Lefebvre
Date: 2024-07-02 00:00:00

This paper introduces a novel adaptation approach for first-order pre- and de-emphasis filters, an essential tool in many speech and audio codecs to increase coding efficiency and perceived quality. The proposed zero-bit self-adaptation approach differs from classical forward and backward adaptation approaches in that the de-emphasis coefficient is estimated at the receiver, from the decoded pre-emphasized signal. This eliminates the need to transmit information that arises from forward adaptation as well as the signal-filter lag that is inherent in backward adaptation. Evaluation results show that the de-emphasis coefficient can be estimated accurately from the decoded pre-emphasized signal and that the proposed zero-bit self-adaptation approach provides comparable subjective improvement to forward adaptation.
SOT Triggered Neural Clustering for Speaker Attributed ASR
Authors: Xianrui Zheng, Guangzhi Sun, Chao Zhang, Philip C. Woodland
Date: 2024-07-02 00:00:00
Tasks: Clustering

This paper introduces a novel approach to speaker-attributed ASR transcription using a neural clustering method. With a parallel processing mechanism, diarisation and ASR can be applied simultaneously, helping to prevent the accumulation of errors from one sub-system to the next in a cascaded system. This is achieved by the use of ASR, trained using a serialised output training method, together with segment-level discriminative neural clustering (SDNC) to assign speaker labels. With SDNC, our system does not require an extra non-neural clustering method to assign speaker labels, thus allowing the entire system to be based on neural networks. Experimental results on the AMI meeting dataset demonstrate that SDNC outperforms spectral clustering (SC) by a 19% relative diarisation error rate (DER) reduction on the AMI Eval set. When compared with the cascaded system with SC, the parallel system with SDNC gives a 7%/4% relative improvement in cpWER on the Dev/Eval set.
Towards Unsupervised Speaker Diarization System for Multilingual Telephone Calls Using Pre-trained Whisper Model and Mixture of Sparse Autoencoders
Authors: Phat Lam, Lam Pham, Truong Nguyen, Dat Ngo, Thinh Pham, Tin Nguyen, Loi Khanh Nguyen, Alexander Schindler
Date: 2024-07-02 00:00:00
Tasks: Clustering, speaker-diarization, Speaker Diarization, Speech-to-Text

Existing speaker diarization systems typically rely on large amounts of manually annotated data, which is labor-intensive and difficult to obtain, especially in real-world scenarios. Additionally, language-specific constraints in these systems significantly hinder their effectiveness and scalability in multilingual settings. In this paper, we propose a cluster-based speaker diarization system designed for multilingual telephone call applications. Our proposed system supports multiple languages and eliminates the need for large-scale annotated data during training by utilizing the multilingual Whisper model to extract speaker embeddings. Additionally, we introduce a network architecture called Mixture of Sparse Autoencoders (Mix-SAE) for unsupervised speaker clustering. Experimental results on the evaluation dataset derived from two-speaker subsets of benchmark CALLHOME and CALLFRIEND telephonic speech corpora demonstrate the superior performance of the proposed Mix-SAE network to other autoencoder-based clustering methods. The overall performance of our proposed system also highlights the promising potential for developing unsupervised, multilingual speaker diarization systems within the context of limited annotated data. It also indicates the system's capability for integration into multi-task speech analysis applications based on general-purpose models such as those that combine speech-to-text, language detection, and speaker diarization.
Unsupervised Face-Masked Speech Enhancement Using Generative Adversarial Networks With Human-in-the-Loop Assessment Metrics
Authors: Syu-Siang Wang, Jia-Yang Chen, Bo-Ren Bai, Shih-Hau Fang, Yu Tsao
Date: 2024-07-02 00:00:00
Tasks: Speech Enhancement

The utilization of face masks is an essential healthcare measure, particularly during times of pandemics, yet it can present challenges in communication in our daily lives. To address this problem, we propose a novel approach known as the human-in-the-loop StarGAN (HL-StarGAN) face-masked speech enhancement method. HL-StarGAN comprises discriminator, classifier, metric assessment predictor, and generator that leverages an attention mechanism. The metric assessment predictor, referred to as MaskQSS, incorporates human participants in its development and serves as a "human-in-the-loop" module during the learning process of HL-StarGAN. The overall HL-StarGAN model was trained using an unsupervised learning strategy that simultaneously focuses on the reconstruction of the original clean speech and the optimization of human perception. To implement HL-StarGAN, we curated a face-masked speech database named "FMVD," which comprises recordings from 34 speakers in three distinct face-masked scenarios and a clean condition. We conducted subjective and objective tests on the proposed HL-StarGAN using this database. The outcomes of the test results are as follows: (1) MaskQSS successfully predicted the quality scores of face mask voices, outperforming several existing speech assessment methods. (2) The integration of the MaskQSS predictor enhanced the ability of HL-StarGAN to transform face mask voices into high-quality speech; this enhancement is evident in both objective and subjective tests, outperforming conventional StarGAN and CycleGAN-based systems.
TTSlow: Slow Down Text-to-Speech with Efficiency Robustness Evaluations
Authors: Xiaoxue Gao, Yiming Chen, Xianghu Yue, Yu Tsao, Nancy F. Chen
Date: 2024-07-02 00:00:00
Tasks: Benchmarking, text-to-speech, Text to Speech

Text-to-speech (TTS) has been extensively studied for generating high-quality speech with textual inputs, playing a crucial role in various real-time applications. For real-world deployment, ensuring stable and timely generation in TTS models against minor input perturbations is of paramount importance. Therefore, evaluating the robustness of TTS models against such perturbations, commonly known as adversarial attacks, is highly desirable. In this paper, we propose TTSlow, a novel adversarial approach specifically tailored to slow down the speech generation process in TTS systems. To induce long TTS waiting time, we design novel efficiency-oriented adversarial loss to encourage endless generation process. TTSlow encompasses two attack strategies targeting both text inputs and speaker embedding. Specifically, we propose TTSlow-text, which utilizes a combination of homoglyphs-based and swap-based perturbations, along with TTSlow-spk, which employs a gradient optimization attack approach for speaker embedding. TTSlow serves as the first attack approach targeting a wide range of TTS models, including autoregressive and non-autoregressive TTS ones, thereby advancing exploration in audio security. Extensive experiments are conducted to evaluate the inference efficiency of TTS models, and in-depth analysis of generated speech intelligibility is performed using Gemini. The results demonstrate that TTSlow can effectively slow down two TTS models across three publicly available datasets. We are committed to releasing the source code upon acceptance, facilitating further research and benchmarking in this domain.
peerRTF: Robust MVDR Beamforming Using Graph Convolutional Network
Authors: Daniel Levi, Amit Sofer, Sharon Gannot
Date: 2024-07-01 00:00:00

Accurate and reliable identification of the relative transfer functions (RTFs) between microphones with respect to a desired source is an essential component in the design of microphone array beamformers, specifically when applying the minimum variance distortionless response (MVDR) criterion. Since an accurate estimation of the RTF in a noisy and reverberant environment is a cumbersome task, we aim at leveraging prior knowledge of the acoustic enclosure to robustify the RTFs estimation by learning the RTF manifold. In this paper, we present a novel robust RTF identification method, tested and trained using both real recordings and simulated scenarios, which relies on learning the RTF manifold using a graph convolutional network (GCN) to infer a robust representation of the RTFs in a confined area, and consequently enhance the beamformers performance.
Audio-Visual Approach For Multimodal Concurrent Speaker Detection
Authors: Amit Eliav, Sharon Gannot
Date: 2024-07-01 00:00:00
Tasks: Multimodal Deep Learning, speaker-diarization, Speaker Diarization, Speech Separation

Concurrent Speaker Detection (CSD), the task of identifying active speakers and their overlaps in an audio signal, is essential for various audio applications, including meeting transcription, speaker diarization, and speech separation. This study presents a multimodal deep learning approach that integrates audio and visual information. The proposed model utilizes an early fusion strategy, combining audio and visual features through cross-modal attention mechanisms with a learnable [CLS] token to capture key audio-visual relationships. The model is extensively evaluated on two real-world datasets, the established AMI dataset and the recently introduced EasyCom dataset. Experiments validate the effectiveness of the multimodal fusion strategy. An ablation study further supports the design choices and the model's training procedure. As this is the first work reporting CSD results on the challenging EasyCom dataset, the findings demonstrate the potential of the proposed multimodal approach for \ac{CSD} in real-world scenarios.
Parametric Modeling and Estimation of Photon Registrations for 3D Imaging
Authors: Weijian Zhang, Hashan K. Weerasooriya, Prateek Chennuri, Stanley H. Chan
Date: 2024-07-02 00:00:00
Tasks: Depth Estimation

In single-photon light detection and ranging (SP-LiDAR) systems, the histogram distortion due to hardware dead time fundamentally limits the precision of depth estimation. To compensate for the dead time effects, the photon registration distribution is typically modeled based on the Markov chain self-excitation process. However, this is a discrete process and it is computationally expensive, thus hindering potential neural network applications and fast simulations. In this paper, we overcome the modeling challenge by proposing a continuous parametric model. We introduce a Gaussian-uniform mixture model (GUMM) and periodic padding to address high noise floors and noise slopes respectively. By deriving and implementing a customized expectation maximization (EM) algorithm, we achieve accurate histogram matching in scenarios that were deemed difficult in the literature.
Deep Harmonic Finesse: Signal Separation in Wearable Systems with Limited Data
Authors: Mahya Saffarpour, Kourosh Vali, Weitai Qian, Begum Kasap, Herman L. Hedriana, Soheil Ghiasi
Date: 2024-07-01 00:00:00

We present a method, referred to as Deep Harmonic Finesse (DHF), for separation of non-stationary quasi-periodic signals when limited data is available. The problem frequently arises in wearable systems in which, a combination of quasi-periodic physiological phenomena give rise to the sensed signal, and excessive data collection is prohibitive. Our approach utilizes prior knowledge of time-frequency patterns in the signals to mask and in-paint spectrograms. This is achieved through an application-inspired deep harmonic neural network coupled with an integrated pattern alignment component. The network's structure embeds the implicit harmonic priors within the time-frequency domain, while the pattern-alignment method transforms the sensed signal, ensuring a strong alignment with the network. The effectiveness of the algorithm is demonstrated in the context of non-invasive fetal monitoring using both synthesized and in vivo data. When applied to the synthesized data, our method exhibits significant improvements in signal-to-distortion ratio (26% on average) and mean squared error (80% on average), compared to the best competing method. When applied to in vivo data captured in pregnant animal studies, our method improves the correlation error between estimated fetal blood oxygen saturation and the ground truth by 80.5% compared to the state of the art.
Unrolling Plug-and-Play Gradient Graph Laplacian Regularizer for Image Restoration
Authors: Jianghe Cai, Gene Cheung, Fei Chen
Date: 2024-07-01 00:00:00
Tasks: Denoising, Graph Learning, Image Restoration

Generic deep learning (DL) networks for image restoration like denoising and interpolation lack mathematical interpretability, require voluminous training data to tune a large parameter set, and are fragile in the face of covariate shift. To address these shortcomings, we build interpretable networks by unrolling variants of a graph-based optimization algorithm of different complexities. Specifically, for a general linear image formation model, we first formulate a convex quadratic programming (QP) problem with a new $\ell_2$-norm graph smoothness prior called gradient graph Laplacian regularizer (GGLR) that promotes piecewise planar (PWP) signal reconstruction. To solve the posed unconstrained QP problem, instead of computing a linear system solution straightforwardly, we introduce a variable number of auxiliary variables and correspondingly design a family of ADMM algorithms. We then unroll them into variable-complexity feed-forward networks, amenable to parameter tuning via back-propagation. More complex unrolled networks require more labeled data to train more parameters, but have better overall performance. The unrolled networks have periodic insertions of a graph learning module, akin to a self-attention mechanism in a transformer architecture, to learn pairwise similarity structure inherent in data. Experimental results show that our unrolled networks perform competitively to generic DL networks in image restoration quality while using only a fraction of parameters, and demonstrate improved robustness to covariate shift.