pwc-archive/papers-with-abstracts huggingface dataset:
China's Rising Leadership in Global Science
Authors: Renli Wu, Christopher Esposito, James Evans
Date: 2024-06-09 00:00:00

Major shifts in the global system of science and technology are destabilizing the global status order and demonstrating the capacity for emerging countries like China and India to exert greater influence. In order to measure changes in the global scientific system, we develop a framework to assess the hierarchical position of countries in the international scientific collaboration network. Using a machine-learning model to identify the leaders of 5,966,623 scientific teams that collaborated across international borders, we show that Chinese scientists substantially narrowed their leadership deficit with scientists from the US, UK, and EU between 1990 and 2023 in absolute terms. Consequently, China and the US are on track to reach an equal number of team leaders engaged in bilateral collaborations between 2027 and 2028. Nevertheless, Chinese progress has been considerably slower in per-collaborator terms: after adjusting for the number of non-leaders from each country, our models do not predict parity between the US and China until after 2087. These dynamics extend to 11 critical technology areas central to ongoing diplomacy between the two nations, such AI, Semiconductors, and Advanced Communications, and to China's scientific leadership with respect to the European Union and the United Kingdom. Thus, while China's elite scientists are achieving leadership in the international scientific community, China's scientific enterprise continues to face developmental constraints. We conclude by reviewing several steps that Chinese science is taking to overcome these constraints, by increasing its engagement in scientific training and research in signatory nations to the Belt and Road Initiative.
Human Learning about AI
Authors: Bnaya Dreyfuss, Raphaël Raux
Date: 2024-06-08 00:00:00
Tasks: Math

We study how people form expectations about the performance of artificial intelligence (AI) and consequences for AI adoption. Our main hypothesis is that people rely on human-relevant task features when evaluating AI, treating AI failures on human-easy tasks, and successes on human-difficult tasks, as highly informative of its overall performance. In lab experiments, we show that projection of human difficulty onto AI predictably distorts subjects' beliefs and can lead to suboptimal adoption, as failing human-easy tasks need not imply poor overall performance for AI. We find evidence for projection in a field experiment with an AI giving parenting advice. Potential users strongly infer from answers that are equally uninformative but less humanly-similar to expected answers, significantly reducing trust and future engagement. Our results suggest AI "anthropomorphism" can backfire by increasing projection and de-aligning people's expectations and AI performance.
Differential Test Performance and Peer Effects
Authors: Guido Kuersteiner, Ingmar Prucha, Ying Zeng
Date: 2024-06-07 00:00:00

We use variation of test scores measuring closely related skills to isolate peer effects. The intuition for our identification strategy is that the difference in closely related scores eliminates factors common to the performance in either test while retaining idiosyncratic test specific variation. Common factors include unobserved teacher and group effects as well as test invariant ability and factors relevant for peer group formation. Peer effects work through idiosyncratic shocks which have the interpretation of individual and test specific ability or effort. We use education production functions as well as restrictions on the information content of unobserved test taking ability to formalize our approach. An important implication of our identifying assumptions is that we do not need to rely on randomized group assignment. We show that our model restrictions are sufficient for the formulation of linear and quadratic moment conditions that identify the peer effects parameter of interest. We use Project STAR data to empirically measure peer effects in kindergarten through third grade classes. We find evidence of highly significant peer effects with magnitudes that are at the lower end of the range of estimates found in the literature.
Early School Leaving in Spain: a longitudinal analysis by gender
Authors: Martín Martín-González, Sara M. González-Betancor, Carmen Pérez-Esparrells
Date: 2024-06-07 00:00:00

Spain is one of the eight EU-27 countries that failed to reduce early school leaving (ESL) below 10% in 2020, and now faces the challenge of achieving a rate below 9% by 2030. The determinants of this phenomenon are usually studied using cross-sectional data at the micro-level and without differentiation by gender. In this study, we analyse it for the first time for Spain using panel data (between 2002-2020), taking into account the high regional inequalities at the macroeconomic level and the masculinisation of the phenomenon. The results show a positive relationship between ESL and socioeconomic variables such as the adolescent fertility rate, immigration, unemployment or the weight of the industrial and construction sectors in the regional economy, with significant gender differences that invite us to discuss educational policies. Surprisingly, youth unemployment has only small but significant impact on female ESL.
A simulation-optimization framework for food supply chain network design to ensure food accessibility under uncertainty
Authors: Mengfei Chen, Mohamed Kharbeche, Mohamed Haouari, Weihong "Grace" Guo
Date: 2024-06-06 00:00:00
Tasks: Nutrition

How to ensure accessibility to food and nutrition while food supply chains suffer from demand and supply uncertainties caused by disruptive forces such as the COVID-19 pandemic and natural disasters is an emerging and critical issue. Unstable access to food influences the level of nutrition that weakens the health and well-being of citizens. Therefore, a food accessibility evaluation index is proposed in this work to quantify how well nutrition needs are met. The proposed index is then embedded in a stochastic multi-objective mixed-integer optimization problem to determine the optimal supply chain design to maximize food accessibility and minimize cost. Considering uncertainty in demand and supply, the multi-objective problem is solved in a two-phase simulation-optimization framework in which Green Field Analysis is applied to determine the long-term, tactical decisions such as supply chain configuration, and then Monte Carlo simulation is performed iteratively to determine the short-term supply chain operations by solving a stochastic programming problem. A case study is conducted on the beef supply chain in Qatar. Pareto efficient solutions are validated in discrete event simulation to evaluate the performance of the designed supply chain in various realistic scenarios and provide recommendations for different decision-makers.
Information Aggregation with Costly Information Acquisition
Authors: Spyros Galanis, Sergei Mikhalishchev
Date: 2024-06-11 00:00:00
Tasks: All

We study information aggregation in a dynamic trading model with partially informed traders. Ostrovsky [2012] showed that 'separable' securities aggregate information in all equilibria, however, separability is not robust to small changes in the traders' private information. To remedy this problem, we enhance the model by allowing traders to acquire signals with cost $\kappa$, in every period. We show that '$\kappa$ separable securities' aggregate information and, as the cost decreases, nearly all securities become $\kappa$ separable, irrespective of the traders' initial private information. Moreover, the switch to $\kappa$ separability happens not gradually but discontinuously, hence even a small decrease in costs can result in a security aggregating information. Finally, even with myopic traders, cheaper information may accelerate or decelerate information aggregation for all but Arrow-Debreu securities.
Third Degree Price Discrimination Under Costly Information Acquisition
Authors: Irfan Tekdir
Date: 2024-06-10 00:00:00
Tasks: Segmentation, valid

This paper investigates third-degree price discrimination under endogenous market segmentation. Segmenting a market requires access to information about consumers, and this information comes with a cost. I explore the trade-offs between the benefits of segmentation and the costs of information acquisition, revealing a non-monotonic relationship between consumer surplus and the cost of information acquisition for monopolist. I show that in some markets, allowing the monopolist easier access to customer data can also benefit customers. I also analyzed how social welfare reacts to changes in the cost level of information acquisition and showed that the non-monotonicity result is also valid in social welfare analysis.
Learning about informativeness
Authors: Wanying Huang
Date: 2024-06-07 00:00:00
Tasks: Informativeness

We study whether individuals can learn the informativeness of their information technology through social learning. As in the classic sequential social learning model, rational agents arrive in order and make decisions based on the past actions of others and their private signals. There is uncertainty regarding the informativeness of the common signal-generating process. We show that learning in this setting is not guaranteed, and depends crucially on the relative tail distributions of private beliefs induced by uninformative and informative signals. We identify the phenomenon of perpetual disagreement as the cause of learning and characterize learning in the canonical Gaussian environment.
Network Threshold Games
Authors: Alastair Langtry, Sarah Taylor, Yifan Zhang
Date: 2024-06-06 00:00:00

This paper studies the general class of games where agents: (1) are embedded on a network, (2) have two possible actions, and (3) these actions are strategic complements. We use a measure of network cohesiveness -- the k-core -- to provide a novel characterisation of the equilibria. After transforming the network appropriately, the k-core fully describes both the minimal and maximal equilibria, and also provides a partial characterisation of all others. This framework is also the binary action version of the large class of network games with strategic complements and continuous actions.
Noise-robust Speech Separation with Fast Generative Correction
Authors: Helin Wang, Jesus Villalba, Laureano Moro-Velazquez, Jiarui Hai, Thomas Thebaud, Najim Dehak
Date: 2024-06-11 00:00:00
Tasks: Speech Separation

Speech separation, the task of isolating multiple speech sources from a mixed audio signal, remains challenging in noisy environments. In this paper, we propose a generative correction method to enhance the output of a discriminative separator. By leveraging a generative corrector based on a diffusion model, we refine the separation process for single-channel mixture speech by removing noises and perceptually unnatural distortions. Furthermore, we optimize the generative model using a predictive loss to streamline the diffusion model's reverse process into a single step and rectify any associated errors by the reverse process. Our method achieves state-of-the-art performance on the in-domain Libri2Mix noisy dataset, and out-of-domain WSJ with a variety of noises, improving SI-SNR by 22-35% relative to SepFormer, demonstrating robustness and strong generalization capabilities.
Single-Codec: Single-Codebook Speech Codec towards High-Performance Speech Generation
Authors: Hanzhao Li, Liumeng Xue, Haohan Guo, Xinfa Zhu, YuanJun Lv, Lei Xie, Yunlin Chen, Hao Yin, Zhifei Li
Date: 2024-06-11 00:00:00

The multi-codebook speech codec enables the application of large language models (LLM) in TTS but bottlenecks efficiency and robustness due to multi-sequence prediction. To avoid this obstacle, we propose Single-Codec, a single-codebook single-sequence codec, which employs a disentangled VQ-VAE to decouple speech into a time-invariant embedding and a phonetically-rich discrete sequence. Furthermore, the encoder is enhanced with 1) contextual modeling with a BLSTM module to exploit the temporal information, 2) a hybrid sampling module to alleviate distortion from upsampling and downsampling, and 3) a resampling module to encourage discrete units to carry more phonetic information. Compared with multi-codebook codecs, e.g., EnCodec and TiCodec, Single-Codec demonstrates higher reconstruction quality with a lower bandwidth of only 304bps. The effectiveness of Single-Code is further validated by LLM-TTS experiments, showing improved naturalness and intelligibility.
Clever Hans Effect Found in Automatic Detection of Alzheimer's Disease through Speech
Authors: Yin-Long Liu, Rui Feng, Jia-Hong Yuan, Zhen-Hua Ling
Date: 2024-06-11 00:00:00

We uncover an underlying bias present in the audio recordings produced from the picture description task of the Pitt corpus, the largest publicly accessible database for Alzheimer's Disease (AD) detection research. Even by solely utilizing the silent segments of these audio recordings, we achieve nearly 100% accuracy in AD detection. However, employing the same methods to other datasets and preprocessed Pitt recordings results in typical levels (approximately 80%) of AD detection accuracy. These results demonstrate a Clever Hans effect in AD detection on the Pitt corpus. Our findings emphasize the crucial importance of maintaining vigilance regarding inherent biases in datasets utilized for training deep learning models, and highlight the necessity for a better understanding of the models' performance.
WenetSpeech4TTS: A 12,800-hour Mandarin TTS Corpus for Large Speech Generation Model Benchmark
Authors: Linhan Ma, Dake Guo, Kun Song, Yuepeng Jiang, Shuai Wang, Liumeng Xue, Weiming Xu, Huan Zhao, BinBin Zhang, Lei Xie
Date: 2024-06-09 00:00:00
Tasks: text-to-speech, Text to Speech

With the development of large text-to-speech (TTS) models and scale-up of the training data, state-of-the-art TTS systems have achieved impressive performance. In this paper, we present WenetSpeech4TTS, a multi-domain Mandarin corpus derived from the open-sourced WenetSpeech dataset. Tailored for the text-to-speech tasks, we refined WenetSpeech by adjusting segment boundaries, enhancing the audio quality, and eliminating speaker mixing within each segment. Following a more accurate transcription process and quality-based data filtering process, the obtained WenetSpeech4TTS corpus contains $12,800$ hours of paired audio-text data. Furthermore, we have created subsets of varying sizes, categorized by segment quality scores to allow for TTS model training and fine-tuning. VALL-E and NaturalSpeech 2 systems are trained and fine-tuned on these subsets to validate the usability of WenetSpeech4TTS, establishing baselines on benchmark for fair comparison of TTS systems. The corpus and corresponding benchmarks are publicly available on huggingface.
Text-aware and Context-aware Expressive Audiobook Speech Synthesis
Authors: Dake Guo, Xinfa Zhu, Liumeng Xue, Yongmao Zhang, Wenjie Tian, Lei Xie
Date: 2024-06-09 00:00:00
Tasks: Contrastive Learning, Language Modeling, Language Modelling, Sentence, Speech Synthesis, text-to-speech, Text to Speech

Recent advances in text-to-speech have significantly improved the expressiveness of synthetic speech. However, a major challenge remains in generating speech that captures the diverse styles exhibited by professional narrators in audiobooks without relying on manually labeled data or reference speech. To address this problem, we propose a text-aware and context-aware(TACA) style modeling approach for expressive audiobook speech synthesis. We first establish a text-aware style space to cover diverse styles via contrastive learning with the supervision of the speech style. Meanwhile, we adopt a context encoder to incorporate cross-sentence information and the style embedding obtained from text. Finally, we introduce the context encoder to two typical TTS models, VITS-based TTS and language model-based TTS. Experimental results demonstrate that our proposed approach can effectively capture diverse styles and coherent prosody, and consequently improves naturalness and expressiveness in audiobook speech synthesis.
Spoken Language Corpora Augmentation with Domain-Specific Voice-Cloned Speech
Authors: Mateusz Czyżnikiewicz, Łukasz Bondaruk, Jakub Kubiak, Adam Wiącek, Łukasz Degórski, Marek Kubis, Paweł Skórzewski
Date: 2024-06-11 00:00:00
Tasks: speech-recognition, Speech Recognition, Voice Cloning

In this paper we study the impact of augmenting spoken language corpora with domain-specific synthetic samples for the purpose of training a speech recognition system. Using both a conventional neural TTS system and a zero-shot one with voice cloning ability we generate speech corpora that vary in the number of voices. We compare speech recognition models trained with addition of different amounts of synthetic data generated using these two methods with a baseline model trained solely on voice recordings. We show that while the quality of voice-cloned dataset is lower, its increased multivoiceity makes it much more effective than the one with only a few voices synthesized with the use of a conventional neural TTS system. Furthermore, our experiments indicate that using low variability synthetic speech quickly leads to saturation in the quality of the ASR whereas high variability speech provides improvement even when increasing total amount of data used for training by 30%.
The Effect of Training Dataset Size on Discriminative and Diffusion-Based Speech Enhancement Systems
Authors: Philippe Gonzalez, Zheng-Hua Tan, Jan Østergaard, Jesper Jensen, Tommy Sonne Alstrøm, Tobias May
Date: 2024-06-10 00:00:00
Tasks: Diversity, Image Generation, Speech Enhancement

The performance of deep neural network-based speech enhancement systems typically increases with the training dataset size. However, studies that investigated the effect of training dataset size on speech enhancement performance did not consider recent approaches, such as diffusion-based generative models. Diffusion models are typically trained with massive datasets for image generation tasks, but whether this is also required for speech enhancement is unknown. Moreover, studies that investigated the effect of training dataset size did not control for the data diversity. It is thus unclear whether the performance improvement was due to the increased dataset size or diversity. Therefore, we systematically investigate the effect of training dataset size on the performance of popular state-of-the-art discriminative and diffusion-based speech enhancement systems in matched conditions. We control for the data diversity by using a fixed set of speech utterances, noise segments and binaural room impulse responses to generate datasets of different sizes. We find that the diffusion-based systems perform the best relative to the discriminative systems in terms of objective metrics with datasets of 10 h or less. However, their objective metrics performance does not improve when increasing the training dataset size as much as the discriminative systems, and they are outperformed by the discriminative systems with datasets of 100 h or more.
Separate and Reconstruct: Asymmetric Encoder-Decoder for Speech Separation
Authors: Ui-Hyeop Shin, Sangyoun Lee, Taehan Kim, Hyung-Min Park
Date: 2024-06-10 00:00:00
Tasks: Chunking, Speech Separation

In speech separation, time-domain approaches have successfully replaced the time-frequency domain with latent sequence feature from a learnable encoder. Conventionally, the feature is separated into speaker-specific ones at the final stage of the network. Instead, we propose a more intuitive strategy that separates features earlier by expanding the feature sequence to the number of speakers as an extra dimension. To achieve this, an asymmetric strategy is presented in which the encoder and decoder are partitioned to perform distinct processing in separation tasks. The encoder analyzes features, and the output of the encoder is split into the number of speakers to be separated. The separated sequences are then reconstructed by the weight-shared decoder, which also performs cross-speaker processing. Without relying on speaker information, the weight-shared network in the decoder directly learns to discriminate features using a separation objective. In addition, to improve performance, traditional methods have extended the sequence length, leading to the adoption of dual-path models, which handle the much longer sequence effectively by segmenting it into chunks. To address this, we introduce global and local Transformer blocks that can directly handle long sequences more efficiently without chunking and dual-path processing. The experimental results demonstrated that this asymmetric structure is effective and that the combination of proposed global and local Transformer can sufficiently replace the role of inter- and intra-chunk processing in dual-path structure. Finally, the presented model combining both of these achieved state-of-the-art performance with much less computation in various benchmark datasets.
BS-PLCNet 2: Two-stage Band-split Packet Loss Concealment Network with Intra-model Knowledge Distillation
Authors: Zihan Zhang, Xianjun Xia, Chuanzeng Huang, Yijian Xiao, Lei Xie
Date: 2024-06-10 00:00:00
Tasks: Knowledge Distillation, Packet Loss Concealment

Audio packet loss is an inevitable problem in real-time speech communication. A band-split packet loss concealment network (BS-PLCNet) targeting full-band signals was recently proposed. Although it performs superiorly in the ICASSP 2024 PLC Challenge, BS-PLCNet is a large model with high computational complexity of 8.95G FLOPS. This paper presents its updated version, BS-PLCNet 2, to reduce computational complexity and improve performance further. Specifically, to compensate for the missing future information, in the wide-band module, we design a dual-path encoder structure (with non-causal and causal path) and leverage an intra-model knowledge distillation strategy to distill the future information from the non-causal teacher to the casual student. Moreover, we introduce a lightweight post-processing module after packet loss restoration to recover speech distortions and remove residual noise in the audio signal. With only 40% of original parameters in BS-PLCNet, BS-PLCNet 2 brings 0.18 PLCMOS improvement on the ICASSP 2024 PLC challenge blind set, achieving state-of-the-art performance on this dataset.
Accent Conversion with Articulatory Representations
Authors: Yashish M. Siriwardena, Nathan Swedlow, Audrey Howard, Evan Gitterman, Dan Darcy, Carol Espy-Wilson, Andrea Fanelli
Date: 2024-06-10 00:00:00
Tasks: Multi-Task Learning

Conversion of non-native accented speech to native (American) English has a wide range of applications such as improving intelligibility of non-native speech. Previous work on this domain has used phonetic posteriograms as the target speech representation to train an acoustic model which is then used to extract a compact representation of input speech for accent conversion. In this work, we introduce the idea of using an effective articulatory speech representation, extracted from an acoustic-to-articulatory speech inversion system, to improve the acoustic model used in accent conversion. The idea to incorporate articulatory representations originates from their ability to well characterize accents in speech. To incorporate articulatory representations with conventional phonetic posteriograms, a multi-task learning based acoustic model is proposed. Objective and subjective evaluations show that the use of articulatory representations can improve the effectiveness of accent conversion.