pwc-archive/papers-with-abstracts huggingface dataset:
An essay on the history of DSGE models
Authors: Genaro Martín Damiani
Date: 2024-09-01 00:00:00
Tasks: valid

Dynamic Stochastic General Equilibrium (DSGE) models are nowadays a crucial quantitative tool for policy-makers. However, they did not emerge spontaneously. They are built upon previously established ideas in Economics and relatively recent advancements in Mathematics. This essay provides a comprehensive coverage of their history, starting from the pioneering Neoclassical general equilibrium theories and eventually reaching the New Neoclassical Synthesis (NNS). In addition, the mathematical tools involved in formulating a DSGE model are thoroughly presented. I argue that this history has a mixed nature rather than an absolutist or relativist one, that the NNS may have emerged due to the complementary nature of New Classical and New Keynesian theories, and that the recent adoption and development of DSGE models by central banks from different countries has entailed a departure from the goal of building a universally valid theory that Economics has always had. The latter means that DSGE modeling has landed not without loss of generality.
Not All Oil Price Shocks Are Alike. A Replication of Kilian (American Economic Review, 2009)
Authors: Rich Ryan, Nyakundi Michieka
Date: 2024-09-01 00:00:00
Tasks: All

The price of oil can rise because of a disruption to supply or an increase in demand. The nature of the price change determines the dynamic effects. As Kilian (2009) put it: "not all oil price shocks are alike." Using the latest available data, we extend Kilian's (2009) analysis using the R ecosystem and provide more evidence for Kilian's (2009) conclusions. Inference based on unknown conditional heteroskedasticity strengthens the conclusions. With the updated shocks, we assess how a local economy responds to the global oil market, an application that is relevant to policymakers concerned with the transition away from fossil fuels.
Does ESG Consistently Promote the Corporate Financial Performance? A Study of the Global Cruise Industry
Authors: Yuechen Wu
Date: 2024-09-01 00:00:00
Tasks: Decision Making

The analysis of determinants of a company's financial performance has aroused significant attention, particularly, the environmental, social, and governance (ESG) has been the research focus in recent years. In addition to increasing revenue, the cruise industry has actively embraced the initiative of "green shipping". This study investigates the relationship between ESG and corporate financial performance (CFP) in the global cruise sector. This paper utilizes the sample data from the world's largest cruise companies over 2012-2023, to examine the ESG-CFP relationship by a regression model. The results indicate that ESG practices in cruise companies negatively influence CFP, which is further impacted by financial constraints. Furthermore, the heterogeneity analysis suggests that the high time interest earned (TIE) ratios and low total annual greenhouse gas (GHG) emissions worsen the adverse impacts of ESG on CFP. These findings contribute to the theoretical research on ESG and provide practical guidance for cruise industry operators and investors in their decision-making.
Stochastic Monotonicity and Random Utility Models: The Good and The Ugly
Authors: Henk Keffert, Nikolaus Schweizer
Date: 2024-09-01 00:00:00
Tasks: valid

When it comes to structural estimation of risk preferences from data on choices, random utility models have long been one of the standard research tools in economics. A recent literature has challenged these models, pointing out some concerning monotonicity and, thus, identification problems. In this paper, we take a second look and point out that some of the criticism - while extremely valid - may have gone too far, demanding monotonicity of choice probabilities in decisions where it is not so clear whether it should be imposed. We introduce a new class of random utility models based on carefully constructed generalized risk premia which always satisfy our relaxed monotonicity criteria. Moreover, we show that some of the models used in applied research like the certainty-equivalent-based random utility model for CARA utility actually lie in this class of monotonic stochastic choice models. We conclude that not all random utility models are bad.
Credit Scores: Performance and Equity
Authors: Stefania Albanesi, Domonkos F. Vamossy
Date: 2024-08-30 00:00:00
Tasks: Credit score

Credit scores are critical for allocating consumer debt in the United States, yet little evidence is available on their performance. We benchmark a widely used credit score against a machine learning model of consumer default and find significant misclassification of borrowers, especially those with low scores. Our model improves predictive accuracy for young, low-income, and minority groups due to its superior performance with low quality data, resulting in a gain in standing for these populations. Our findings suggest that improving credit scoring performance could lead to more equitable access to credit.
Spatial-temporal evolution characteristics and driving factors of carbon emission prediction in China-research on ARIMA-BP neural network algorithm
Authors: Zhao Sanglin, Li Zhetong, Deng Hao, You Xing, Tong Jiaang, Yuan Bingkun, Zeng Zihao
Date: 2024-08-18 00:00:00

China accounts for one-third of the world's total carbon emissions. How to reach the peak of carbon emissions by 2030 and achieve carbon neutrality by 2060 to ensure the effective realization of the "dual-carbon" target is an important policy orientation at present. Based on the provincial panel data of ARIMA-BP model, this paper shows that the effect of energy consumption intensity effect is the main factor driving the growth of carbon emissions, per capita GDP and energy consumption structure effect are the main factors to inhibit carbon emissions, and the effect of industrial structure and population size effect is relatively small. Based on the research conclusion, the policy suggestions are put forward from the aspects of energy structure, industrial structure, new quality productivity and digital economy.
Strictly Proper Scoring Mechanisms Without Expected Arbitrage
Authors: Jack Edwards
Date: 2024-09-11 00:00:00

When eliciting forecasts from a group of experts, it is important to reward predictions so that market participants are incentivized to tell the truth. Existing mechanisms partially accomplish this but remain susceptible to groups of experts colluding to increase their expected reward, meaning that no aggregation of predictions can be fully trusted to represent the true beliefs of forecasters. This paper presents two novel scoring mechanisms which elicit truthful forecasts from any group of experts, even if they can collude or access each other's predictions. The key insight of this approach is a randomization component which maintains strict properness but prevents experts from coordinating dishonest reports in advance. These mechanisms are strictly proper and do not admit expected arbitrage, resolving an open question in the field.
Obvious Strategy-proofness with Respect to a Partition
Authors: R. Pablo Arribillaga, Jordi Massó, Alejandro Neme
Date: 2024-09-09 00:00:00

We define and study obvious strategy-proofness with respect to a partition of the set of agents. It encompasses strategy-proofness as a special case when the partition is the coarsest one and obvious strategy-proofness when the partition is the finest. For any partition, it falls between these two extremes. We establish two general properties of this new notion and apply it to the simple anonymous voting problem with two alternatives and strict preferences.
Uniform price auction with quantity constraints
Authors: Kiho Yoon
Date: 2024-09-06 00:00:00

We study the equilibria of uniform price auctions where bidders have flat demands up to their respective quantity constraints. We present an iterative procedure that systematically finds a Nash equilibrium outcome under semi-complete information as well as a novel ascending auction under incomplete information that has this outcome as a dominant strategy equilibrium. Demand reduction and low price equilibrium may occur since it is sometimes advantageous for a bidder to give up some of his/her demand and get the remaining demand at a low price rather than to get his/her entire demand at a higher price.
Optimal allocations with capacity constrained verification
Authors: Albin Erlanson, Andreas Kleiner
Date: 2024-09-03 00:00:00
Tasks: Object

A principal has $m$ identical objects to allocate among a group of $n$ agents. Objects are desirable and the principal's value of assigning an object to an agent is the agent's private information. The principal can verify up to $k$ agents, where $k<m$, thereby perfectly learning the types of those verified. We find the mechanism that maximizes the principal's expected utility when no monetary transfers are available. In this mechanism, an agent receives an object if (i) his type is above a cutoff and among the $m$ highest types, (ii) his type is above some lower cutoff but among the $k$ highest types, or (iii) he receives an object in a lottery that allocates the remaining objects randomly.
Hierarchical Symbolic Pop Music Generation with Graph Neural Networks
Authors: Wen Qing Lim, Jinhua Liang, huan zhang
Date: 2024-09-12 00:00:00
Tasks: Music Generation, Rhythm

Music is inherently made up of complex structures, and representing them as graphs helps to capture multiple levels of relationships. While music generation has been explored using various deep generation techniques, research on graph-related music generation is sparse. Earlier graph-based music generation worked only on generating melodies, and recent works to generate polyphonic music do not account for longer-term structure. In this paper, we explore a multi-graph approach to represent both the rhythmic patterns and phrase structure of Chinese pop music. Consequently, we propose a two-step approach that aims to generate polyphonic music with coherent rhythm and long-term structure. We train two Variational Auto-Encoder networks - one on a MIDI dataset to generate 4-bar phrases, and another on song structure labels to generate full song structure. Our work shows that the models are able to learn most of the structural nuances in the training dataset, including chord and pitch frequency distributions, and phrase attributes.
Dark Experience for Incremental Keyword Spotting
Authors: Tianyi Peng, Yang Xiao
Date: 2024-09-12 00:00:00
Tasks: Continual Learning, Keyword Spotting

Spoken keyword spotting (KWS) is crucial for identifying keywords within audio inputs and is widely used in applications like Apple Siri and Google Home, particularly on edge devices. Current deep learning-based KWS systems, which are typically trained on a limited set of keywords, can suffer from performance degradation when encountering new domains, a challenge often addressed through few-shot fine-tuning. However, this adaptation frequently leads to catastrophic forgetting, where the model's performance on original data deteriorates. Progressive continual learning (CL) strategies have been proposed to overcome this, but they face limitations such as the need for task-ID information and increased storage, making them less practical for lightweight devices. To address these challenges, we introduce Dark Experience for Keyword Spotting (DE-KWS), a novel CL approach that leverages dark knowledge to distill past experiences throughout the training process. DE-KWS combines rehearsal and distillation, using both ground truth labels and logits stored in a memory buffer to maintain model performance across tasks. Evaluations on the Google Speech Command dataset show that DE-KWS outperforms existing CL baselines in average accuracy without increasing model size, offering an effective solution for resource-constrained edge devices. The scripts are available on GitHub for the future research.
Auto-Landmark: Acoustic Landmark Dataset and Open-Source Toolkit for Landmark Extraction
Authors: Xiangyu Zhang, Daijiao Liu, Tianyi Xiao, Cihan Xiao, Tuende Szalay, Mostafa Shahin, Beena Ahmed, Julien Epps
Date: 2024-09-12 00:00:00
Tasks: Depression Detection, speech-recognition, Speech Recognition

In the speech signal, acoustic landmarks identify times when the acoustic manifestations of the linguistically motivated distinctive features are most salient. Acoustic landmarks have been widely applied in various domains, including speech recognition, speech depression detection, clinical analysis of speech abnormalities, and the detection of disordered speech. However, there is currently no dataset available that provides precise timing information for landmarks, which has been proven to be crucial for downstream applications involving landmarks. In this paper, we selected the most useful acoustic landmarks based on previous research and annotated the TIMIT dataset with them, based on a combination of phoneme boundary information and manual inspection. Moreover, previous landmark extraction tools were not open source or benchmarked, so to address this, we developed an open source Python-based landmark extraction tool and established a series of landmark detection baselines. The first of their kinds, the dataset with landmark precise timing information, landmark extraction tool and baselines are designed to support a wide variety of future research.
Detecting and Defending Against Adversarial Attacks on Automatic Speech Recognition via Diffusion Models
Authors: Nikolai L. Kühne, Astrid H. F. Kitchen, Marie S. Jensen, Mikkel S. L. Brøndt, Martin Gonzalez, Christophe Biscio, Zheng-Hua Tan
Date: 2024-09-12 00:00:00
Tasks: Adversarial Attack, Adversarial Purification, Automatic Speech Recognition, Speech Recognition

Automatic speech recognition (ASR) systems are known to be vulnerable to adversarial attacks. This paper addresses detection and defence against targeted white-box attacks on speech signals for ASR systems. While existing work has utilised diffusion models (DMs) to purify adversarial examples, achieving state-of-the-art results in keyword spotting tasks, their effectiveness for more complex tasks such as sentence-level ASR remains unexplored. Additionally, the impact of the number of forward diffusion steps on performance is not well understood. In this paper, we systematically investigate the use of DMs for defending against adversarial attacks on sentences and examine the effect of varying forward diffusion steps. Through comprehensive experiments on the Mozilla Common Voice dataset, we demonstrate that two forward diffusion steps can completely defend against adversarial attacks on sentences. Moreover, we introduce a novel, training-free approach for detecting adversarial attacks by leveraging a pre-trained DM. Our experimental results show that this method can detect adversarial attacks with high accuracy.
Rethinking Mamba in Speech Processing by Self-Supervised Models
Authors: Xiangyu Zhang, Jianbo Ma, Mostafa Shahin, Beena Ahmed, Julien Epps
Date: 2024-09-11 00:00:00
Tasks: Mamba, Speech Enhancement, speech-recognition, Speech Recognition

The Mamba-based model has demonstrated outstanding performance across tasks in computer vision, natural language processing, and speech processing. However, in the realm of speech processing, the Mamba-based model's performance varies across different tasks. For instance, in tasks such as speech enhancement and spectrum reconstruction, the Mamba model performs well when used independently. However, for tasks like speech recognition, additional modules are required to surpass the performance of attention-based models. We propose the hypothesis that the Mamba-based model excels in "reconstruction" tasks within speech processing. However, for "classification tasks" such as Speech Recognition, additional modules are necessary to accomplish the "reconstruction" step. To validate our hypothesis, we analyze the previous Mamba-based Speech Models from an information theory perspective. Furthermore, we leveraged the properties of HuBERT in our study. We trained a Mamba-based HuBERT model, and the mutual information patterns, along with the model's performance metrics, confirmed our assumptions.
Neural Ambisonic Encoding For Multi-Speaker Scenarios Using A Circular Microphone Array
Authors: Yue Qiao, Vinay Kothapally, Meng Yu, Dong Yu
Date: 2024-09-11 00:00:00

Spatial audio formats like Ambisonics are playback device layout-agnostic and well-suited for applications such as teleconferencing and virtual reality. Conventional Ambisonic encoding methods often rely on spherical microphone arrays for efficient sound field capture, which limits their flexibility in practical scenarios. We propose a deep learning (DL)-based approach, leveraging a two-stage network architecture for encoding circular microphone array signals into second-order Ambisonics (SOA) in multi-speaker environments. In addition, we introduce: (i) a novel loss function based on spatial power maps to regularize inter-channel correlations of the Ambisonic signals, and (ii) a channel permutation technique to resolve the ambiguity of encoding vertical information using a horizontal circular array. Evaluation on simulated speech and noise datasets shows that our approach consistently outperforms traditional signal processing (SP) and DL-based methods, providing significantly better timbral and spatial quality and higher source localization accuracy. Binaural audio demos with visualizations are available at https://bridgoon97.github.io/NeuralAmbisonicEncoding/.
AS-Speech: Adaptive Style For Speech Synthesis
Authors: Zhipeng Li, Xiaofen Xing, Jun Wang, Shuaiqi Chen, Guoqiao Yu, Guanglu Wan, Xiangmin Xu
Date: 2024-09-09 00:00:00
Tasks: Rhythm, Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis

In recent years, there has been significant progress in Text-to-Speech (TTS) synthesis technology, enabling the high-quality synthesis of voices in common scenarios. In unseen situations, adaptive TTS requires a strong generalization capability to speaker style characteristics. However, the existing adaptive methods can only extract and integrate coarse-grained timbre or mixed rhythm attributes separately. In this paper, we propose AS-Speech, an adaptive style methodology that integrates the speaker timbre characteristics and rhythmic attributes into a unified framework for text-to-speech synthesis. Specifically, AS-Speech can accurately simulate style characteristics through fine-grained text-based timbre features and global rhythm information, and achieve high-fidelity speech synthesis through the diffusion model. Experiments show that the proposed model produces voices with higher naturalness and similarity in terms of timbre and rhythm compared to a series of adaptive TTS models.
An investigation of modularity for noise robustness in conformer-based ASR
Authors: Louise Coppieters de Gibson, Philip N. Garner, Pierre-Edouard Honnet
Date: 2024-09-09 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Whilst state of the art automatic speech recognition (ASR) can perform well, it still degrades when exposed to acoustic environments that differ from those used when training the model. Unfamiliar environments for a given model may well be known a-priori, but yield comparatively small amounts of adaptation data. In this experimental study, we investigate to what extent recent formalisations of modularity can aid adaptation of ASR to new acoustic environments. Using a conformer based model and fixed routing, we confirm that environment awareness can indeed lead to improved performance in known environments. However, at least on the (CHIME) datasets in the study, it is difficult for a classifier module to distinguish different noisy environments, a simpler distinction between noisy and clean speech being the optimal configuration. The results have clear implications for deploying large models in particular environments with or without a-priori knowledge of the environmental noise.
Leveraging Content and Acoustic Representations for Speech Emotion Recognition
Authors: Soumya Dutta, Sriram Ganapathy
Date: 2024-09-09 00:00:00
Tasks: Emotion Recognition, Language Modelling, Large Language Model, Speech Emotion Recognition

Speech emotion recognition (SER), the task of identifying the expression of emotion from spoken content, is challenging due to the difficulty in extracting representations that capture emotional attributes from speech. The scarcity of labeled datasets further complicates the challenge where large models are prone to over-fitting. In this paper, we propose CARE (Content and Acoustic Representations of Emotions), where we design a dual encoding scheme which emphasizes semantic and acoustic factors of speech. While the semantic encoder is trained using distillation from utterance-level text representations, the acoustic encoder is trained to predict low-level frame-wise features of the speech signal. The proposed dual encoding scheme is a base-sized model trained only on unsupervised raw speech. With a simple light-weight classification model trained on the downstream task, we show that the CARE embeddings provide effective emotion recognition on a variety of datasets. We compare the proposal with several other self-supervised models as well as recent large-language model based approaches. In these evaluations, the proposed CARE is shown to be the best performing model based on average performance across 8 diverse datasets. We also conduct several ablation studies to analyze the importance of various design choices.
Breaking Walls: Pioneering Automatic Speech Recognition for Central Kurdish: End-to-End Transformer Paradigm
Authors: Abdulhady Abas Abdullah, Hadi Veisi, Tarik Rashid
Date: 2024-04-23 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modeling, Language Modelling, speech-recognition, Speech Recognition

End-to-end transformer-based models epitomize the cutting-edge in Automatic Speech Recognition (ASR) systems. Despite their substantial benefits, these models demand extensive training data to perform optimally, presenting a significant challenge for low-resource languages such as Central Kurdish. Addressing this issue requires innovative methods and techniques. This paper aims to develop an ASR system for Intermediate Kurdish by collecting a robust corpus of speech, using the N-GRAM language model, and utilizing an external Kurdish tokenizer for refinement and integration techniques to enhance the model's performance. We collect a comprehensive 100-hour speech corpus from diverse sources. Additionally, applied fine-tuning techniques to our speech corpus on Persian, English, and Arabic pre-trained models, specifically utilizing the xls-r-300m, xls-r-1b, and xls-r-2b Wav2vec 2.0 models. And utilized language models trained by 3-gram and 4-gram from a large text corpus of 300 million tokens. The fine-tuned xls-r-2b model, combined with a 3-gram language model and included external Kurdish tokenizer, achieved the best performance, yielding a Word Error Rate (WER) of 10.0% on the validation set and 11.8% on the Asosoft test set. The ASR model has demonstrated the advantages of having a large vocabulary compared to the existing Kurdish ASR models. Compared to other models, it produced more accurate and higher performance outcomes by working with a lower error rate.