pwc-archive/papers-with-abstracts huggingface dataset:
Optimal Information Acquisition Strategies: The Case of Online Lending
Authors: Mendelson Haim, Zhu Mingxi
Date: 2024-10-07 00:00:00

Online lending has garnered significant attention in IS literature, particularly platform lending, but direct (balance sheet) lending is increasingly critical. This paper explores optimal information acquisition strategies for direct online lenders, addressing the broader question: Should a decision-maker rely on multiple lean experiments or opt for a single grand experiment? We first examine a model where an online lender issuing unsecured loans maximizes its expected NPV at an exogenous interest rate, finding that a lean experimentation strategy is optimal. However, when the interest rate is endogenous, the choice between lean and grand experimentation depends on the demand elasticity. If elasticity is increasing or constant, the lender prefers a grand experiment, offering the same loan terms in each period. We also analyze consumer segmentation and demonstrate how higher income variability benefits the lender through more effective experimentation. In addition, we investigate hybrid information architectures that combine dynamic experimentation with traditional static models. Our results show that the hybrid architecture enhances lender profitability, offering a flexible approach that integrates sequential learning with static information. The study contributes to understanding how different information architectures affect lending strategies, experimentation, and profitability in online lending.
Persuasion with Ambiguous Communication
Authors: Xiaoyu Cheng, Peter Klibanoff, Sujoy Mukerji, Ludovic Renou
Date: 2024-10-07 00:00:00

This paper explores whether and to what extent ambiguous communication can be beneficial to the sender in a persuasion problem, when the receiver (and possibly the sender) is ambiguity averse. We provide a concavification-like characterization of the sender's optimal ambiguous communication. The characterization highlights the necessity of using a collection of experiments that form a splitting of an obedient (i.e., incentive compatible) experiment. Some experiments in the collection must be Pareto-ranked in the sense that both players agree on their payoff ranking. The existence of a binary such Pareto-ranked splitting is necessary for ambiguous communication to benefit the sender, and, if an optimal Bayesian persuasion experiment can be split in this way, this is sufficient for an ambiguity-neutral sender as well as the receiver to benefit. Such gains are impossible when the receiver has only two actions. The possibility of gains is substantially robust to (non-extreme) sender ambiguity aversion.
Anonymity and strategy-proofness on a domain of single-peaked and single-dipped preferences
Authors: Oihane Gallo
Date: 2024-10-04 00:00:00

We analyze the problem of locating a public facility on a line in a society where agents have either single-peaked or single-dipped preferences. We consider the domain analyzed in Alcalde-Unzu et al. (2024), where the type of preference of each agent is public information, but the location of her peak/dip as well as the rest of the preference are unknown. We characterize all strategy-proof and type-anonymous rules on this domain. Building on existing results, we provide a two-step characterization": first, the median between the peaks and a collection of fixed values is computed (Moulin, 1980), resulting in either a single alternative or a pair of contiguous alternatives. If the outcome of the median is a pair, we apply a double-quota majority method" in the second step to choose between the two alternatives in the pair (Moulin, 1983). We also show the additional conditions that type-anonymity imposes on the strategy-proof rules characterized by Alcalde-Unzu et al. (2024). Finally, we show the equivalence between the two characterizations.
Dynamic Competition for Attention
Authors: Jan Knoepfle
Date: 2024-09-27 00:00:00

This paper studies information transmission from multiple senders who compete for the attention of a decision maker. Each sender is partially informed about the state of the world and decides how to reveal her information over time to maximise attention. A decision maker wants to learn about the state but faces an attention cost. We derive a condition on the informational environment and the decision problem that guarantees that all information from the senders can be transmitted to the decision maker in equilibrium. A simple class of information processes implements full transmission across general environments. The attention each sender receives is proportional to the residual value of her information. In the case of conditionally iid-informed senders, in the limit as the number of senders grows large, the receiver learns the state exactly and immediately (at no attention cost).
Continuity and Monotonicity of Preferences and Probabilistic Equivalence
Authors: Sushil Bikhchandani, Uzi Segal
Date: 2024-09-26 00:00:00
Tasks: Form

We show that probabilistic equivalence of a regret-based preference relationship over random variables is implied by a weak form of continuity and monotonicity.
The Political Economy of Zero-Sum Thinking
Authors: S. Nageeb Ali, Maximilian Mihm, Lucas Siga
Date: 2024-09-24 00:00:00

This paper offers a strategic rationale for zero-sum thinking in elections. We show that asymmetric information and distributional considerations together make voters wary of policies supported by others. This force impels a majority of voters to support policies contrary to their preferences and information. Our analysis identifies and interprets a form of "adverse correlation" that is necessary and sufficient for zero-sum thinking to prevail in equilibrium.
Deep Learning Across Games
Authors: Daniele Condorelli, Massimiliano Furlan
Date: 2024-09-23 00:00:00
Tasks: Deep Learning

We train two neural networks adversarially to play static games. At each iteration, a row and column network observe a new random bimatrix game and output individual mixed strategies. The parameters of each network are independently updated via stochastic gradient descent on a loss defined by the individual squared regret experienced in the game. Simulations show the joint behavior of the trained networks approximates a Nash equilibrium in all games. In $2\times2$ games with multiple equilibria, the networks select the risk dominant equilibrium. These findings, which are robust and generalise out-of-distribution, illustrate how equilibrium emerges from learning across heterogeneous games.
Competitive Markets with Imperfectly Discerning Consumers
Authors: Yair Antler ad Ran Spiegler
Date: 2024-09-23 00:00:00

We develop a market model in which products generate state-dependent potential hidden charges. Firms differ in their ability to realize this potential. Unlike firms, consumers do not observe the state. They try to infer hidden charges from market prices, using idiosyncratic subjective models. We show that an interior competitive equilibrium is uniquely given by what is formally a Bellman equation. We leverage this representation to characterize equilibrium headline prices, add-on charges and welfare. Market responses to shocks display patterns that are impossible under rational expectations. For example, equilibrium prices can be fully revealing and yet vary with consumers' private information.
Sequential Network Design
Authors: Yang Sun, Wei Zhao, Junjie Zhou
Date: 2024-09-21 00:00:00

We study dynamic network formation from a centralized perspective. In each period, the social planner builds a single link to connect previously unlinked pairs. The social planner is forward-looking, with instantaneous utility monotonic in the aggregate number of walks of various lengths. We show that, forming a nested split graph at each period is optimal, regardless of the discount function. When the social planner is sufficiently myopic, it is optimal to form a quasi-complete graph at each period, which is unique up to permutation. This finding provides a micro-foundation for the quasi-complete graph, as it is formed under a greedy policy. We also investigate the robustness of these findings under non-linear best response functions and weighted networks.
A knapsack for collective decision-making
Authors: Yurun Ge, Lucas Böttcher, Tom Chou, Maria R. D'Orsogna
Date: 2024-09-20 00:00:00
Tasks: Decision Making, Decision Making Under Uncertainty

Collective decision-making is the process through which diverse stakeholders reach a joint decision. Within societal settings, one example is participatory budgeting, where constituents decide on the funding of public projects. How to most efficiently aggregate diverse stakeholder inputs on a portfolio of projects with uncertain long-term benefits remains an open question. We address this problem by studying collective decision-making through the integration of preference aggregation and knapsack allocation methods. Since different stakeholder groups may evaluate projects differently,we examine several aggregation methods that combine their diverse inputs. The aggregated evaluations are then used to fill a ``collective'' knapsack. Among the methods we consider are the arithmetic mean, Borda-type rankings, and delegation to experts. We find that the factors improving an aggregation method's ability to identify projects with the greatest expected long-term value include having many stakeholder groups, moderate variation in their expertise levels, and some degree of delegation or bias favoring groups better positioned to objectively assess the projects. We also discuss how evaluation errors and heterogeneous costs impact project selection. Our proposed aggregation methods are relevant not only in the context of funding public projects but also, more generally, for organizational decision-making under uncertainty.
Efficient training strategies for natural sounding speech synthesis and speaker adaptation based on FastPitch
Authors: Teodora Răgman, Adriana Stan
Date: 2024-10-09 00:00:00
Tasks: Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis

This paper focuses on adapting the functionalities of the FastPitch model to the Romanian language; extending the set of speakers from one to eighteen; synthesising speech using an anonymous identity; and replicating the identities of new, unseen speakers. During this work, the effects of various configurations and training strategies were tested and discussed, along with their advantages and weaknesses. Finally, we settled on a new configuration, built on top of the FastPitch architecture, capable of producing natural speech synthesis, for both known (identities from the training dataset) and unknown (identities learnt through short reference samples) speakers. The anonymous speaker can be used for text-to-speech synthesis, if one wants to cancel out the identity information while keeping the semantic content whole and clear. At last, we discussed possible limitations of our work, which will form the basis for future investigations and advancements.
Exploring rhythm formant analysis for Indic language classification
Authors: Parismita Gogoi, Sishir Kalita, Priyankoo Sarmah, S. R Mahadeva Prasanna
Date: 2024-10-08 00:00:00
Tasks: Classification, Rhythm

This paper reports a preliminary study on quantitative frequency domain rhythm cues for classifying five Indian languages: Bengali, Kannada, Malayalam, Marathi, and Tamil. We employ rhythm formant (R-formants) analysis, a technique introduced by Gibbon that utilizes low-frequency spectral analysis of amplitude modulation and frequency modulation envelopes to characterize speech rhythm. Various measures are computed from the LF spectrum, including R-formants, discrete cosine transform-based measures, and spectral measures. Results show that threshold-based and spectral features outperform directly computed R-formants. Temporal pattern of rhythm derived from LF spectrograms provides better language-discriminating cues. Combining all derived features we achieve an accuracy of 69.21% and a weighted F1 score of 69.18% in classifying the five languages. This study demonstrates the potential of RFA in characterizing speech rhythm for Indian language classification.
Improving Data Augmentation-based Cross-Speaker Style Transfer for TTS with Singing Voice, Style Filtering, and F0 Matching
Authors: Leonardo B. de M. M. Marques, Lucas H. Ueda, Mário U. Neto, Flávio O. Simões, Fernando Runstein, Bianca Dal Bó, Paula D. P. Costa
Date: 2024-10-08 00:00:00
Tasks: Data Augmentation, Style Transfer, Voice Conversion

The goal of cross-speaker style transfer in TTS is to transfer a speech style from a source speaker with expressive data to a target speaker with only neutral data. In this context, we propose using a pre-trained singing voice conversion (SVC) model to convert the expressive data into the target speaker's voice. In the conversion process, we apply a fundamental frequency (F0) matching technique to mitigate tonal variances between speakers with significant timbral differences. A style classifier filter is proposed to select the most expressive output audios for the TTS training. Our approach is comparable to state-of-the-art with only a few minutes of neutral data from the target speaker, while other methods require hours. A perceptual assessment showed improvements brought by the SVC and the style filter in naturalness and style intensity for the styles that display more vocal effort. Also, increased speaker similarity is obtained with the proposed F0 matching algorithm.
HALL-E: Hierarchical Neural Codec Language Model for Minute-Long Zero-Shot Text-to-Speech Synthesis
Authors: Yuto Nishimura, Takumi Hirose, Masanari Ohi, Hideki Nakayama, Nakamasa Inoue
Date: 2024-10-06 00:00:00
Tasks: Language Modeling, Language Modelling, Quantization, Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis

Recently, Text-to-speech (TTS) models based on large language models (LLMs) that translate natural language text into sequences of discrete audio tokens have gained great research attention, with advances in neural audio codec (NAC) models using residual vector quantization (RVQ). However, long-form speech synthesis remains a significant challenge due to the high frame rate, which increases the length of audio tokens and makes it difficult for autoregressive language models to generate audio tokens for even a minute of speech. To address this challenge, this paper introduces two novel post-training approaches: 1) Multi-Resolution Requantization (MReQ) and 2) HALL-E. MReQ is a framework to reduce the frame rate of pre-trained NAC models. Specifically, it incorporates multi-resolution residual vector quantization (MRVQ) module that hierarchically reorganizes discrete audio tokens through teacher-student distillation. HALL-E is an LLM-based TTS model designed to predict hierarchical tokens of MReQ. Specifically, it incorporates the technique of using MRVQ sub-modules and continues training from a pre-trained LLM-based TTS model. Furthermore, to promote TTS research, we create MinutesSpeech, a new benchmark dataset consisting of 40k hours of filtered speech data for training and evaluating speech synthesis ranging from 3s up to 180s. In experiments, we demonstrated the effectiveness of our approaches by applying our post-training framework to VALL-E. We achieved the frame rate down to as low as 8 Hz, enabling the stable minitue-long speech synthesis in a single inference step. Audio samples, dataset, codes and pre-trained models are available at https://yutonishimura-v2.github.io/HALL-E_DEMO/.
DJ Mix Transcription with Multi-Pass Non-Negative Matrix Factorization
Authors: Étienne Paul André, Dominique Fourer, Diemo Schwarz
Date: 2024-10-05 00:00:00
Tasks: Dynamic Time Warping

DJ mix transcription is a crucial step towards DJ mix reverse engineering, which estimates the set of parameters and audio effects applied to a set of existing tracks to produce a performative DJ mix. We introduce a new approach based on a multi-pass NMF algorithm where the dictionary matrix corresponds to a set of spectrogram slices of the source tracks present in the mix. The multi-pass strategy is motivated by the high computational cost resulting from the use of a large NMF dictionary. The proposed method uses inter-pass filtering to favor temporal continuity and sparseness and is evaluated on a publicly available dataset. Our comparative results considering a baseline method based on dynamic time warping (DTW) are promising and pave the way of future NMF-based applications.
Enhancement of Dysarthric Speech Reconstruction by Contrastive Learning
Authors: Keshvari Fatemeh, Mahdian Toroghi Rahil, Zareian Hassan
Date: 2024-10-05 00:00:00
Tasks: Contrastive Learning, speech-recognition, Speech Recognition

Dysarthric speech reconstruction is challenging due to its pathological sound patterns. Preserving speaker identity, especially without access to normal speech, is a key challenge. Our proposed approach uses contrastive learning to extract speaker embedding for reconstruction, while employing XLS-R representations instead of filter banks. The results show improved speech quality, naturalness, intelligibility, speaker identity preservation, and gender consistency for female speakers. Reconstructed speech exhibits 1.51 and 2.12 MOS score improvements and reduces word error rates by 25.45% and 32.1% for moderate and moderate-severe dysarthria speakers using Jasper speech recognition system, respectively. This approach offers promising advancements in dysarthric speech reconstruction.
Textless Streaming Speech-to-Speech Translation using Semantic Speech Tokens
Authors: Jinzheng Zhao, Niko Moritz, Egor Lakomkin, Ruiming Xie, Zhiping Xiu, Katerina Zmolikova, Zeeshan Ahmed, Yashesh Gaur, Duc Le, Christian Fuegen
Date: 2024-10-04 00:00:00
Tasks: Language Modeling, Language Modelling, Machine Translation, Speech-to-Speech Translation, text-to-speech, Text to Speech, Translation

Cascaded speech-to-speech translation systems often suffer from the error accumulation problem and high latency, which is a result of cascaded modules whose inference delays accumulate. In this paper, we propose a transducer-based speech translation model that outputs discrete speech tokens in a low-latency streaming fashion. This approach eliminates the need for generating text output first, followed by machine translation (MT) and text-to-speech (TTS) systems. The produced speech tokens can be directly used to generate a speech signal with low latency by utilizing an acoustic language model (LM) to obtain acoustic tokens and an audio codec model to retrieve the waveform. Experimental results show that the proposed method outperforms other existing approaches and achieves state-of-the-art results for streaming translation in terms of BLEU, average latency, and BLASER 2.0 scores for multiple language pairs using the CVSS-C dataset as a benchmark.
State-of-the-art Embeddings with Video-free Segmentation of the Source VoxCeleb Data
Authors: Sara Barahona, Ladislav Mošner, Themos Stafylakis, Oldřich Plchot, Junyi Peng, Lukáš Burget, Jan Černocký
Date: 2024-10-03 00:00:00
Tasks: Speaker Verification

In this paper, we refine and validate our method for training speaker embedding extractors using weak annotations. More specifically, we use only the audio stream of the source VoxCeleb videos and the names of the celebrities without knowing the time intervals in which they appear in the recording. We experiment with hyperparameters and embedding extractors based on ResNet and WavLM. We show that the method achieves state-of-the-art results in speaker verification, comparable with training the extractors in a standard supervised way on the VoxCeleb dataset. We also extend it by considering segments belonging to unknown speakers appearing alongside the celebrities, which are typically being discarded. Overall, our approach can be used for directly training state-of-the-art embedding extractors or as an alternative to the VoxCeleb-like pipeline for dataset creation without needing image modality.
Wanna hear your voice? A sample is all we need!
Authors: The Hieu Pham, Phuong Thanh Tran Nguyen, Xuan Tho Nguyen, Tan Dat Nguyen, Duc Dung Nguyen
Date: 2024-10-01 00:00:00
Tasks: All, Speech Separation, Target Speaker Extraction

Research on audio clue-based target speaker extraction (TSE) has focused on modeling mixtures and reference speech, achieving strong results in English due to abundant datasets. However, cross-lingual properties remain underexplored, as low-resource languages face challenges from limited annotated data and linguistic resources. To bridge this gap, we propose WHYV (Wanna Hear Your Voice), a cross-lingual TSE framework enabling zero-shot adaptation without fine-tuning. WHYV employs a frequency-modulated gating mechanism that dynamically adjusts the acoustic features of the target speaker, minimizing reliance on language-specific cues. Evaluations demonstrate state-of-the-art zero-shot performance: 13.8 dB (Libri2Mix mix-both), 18.1 dB (mix-clean), and 14.8 dB on Vietnamese data.
End-to-End Speech Recognition with Pre-trained Masked Language Model
Authors: Yosuke Higuchi, Tetsuji Ogawa, Tetsunori Kobayashi, Shinji Watanabe
Date: 2024-10-01 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Decoder, Language Modeling, Language Modelling, speech-recognition, Speech Recognition

We present a novel approach to end-to-end automatic speech recognition (ASR) that utilizes pre-trained masked language models (LMs) to facilitate the extraction of linguistic information. The proposed models, BERT-CTC and BECTRA, are specifically designed to effectively integrate pre-trained LMs (e.g., BERT) into end-to-end ASR models. BERT-CTC adapts BERT for connectionist temporal classification (CTC) by addressing the constraint of the conditional independence assumption between output tokens. This enables explicit conditioning of BERT's contextualized embeddings in the ASR process, seamlessly merging audio and linguistic information through an iterative refinement algorithm. BECTRA extends BERT-CTC to the transducer framework and trains the decoder network using a vocabulary suitable for ASR training. This aims to bridge the gap between the text processed in end-to-end ASR and BERT, as these models have distinct vocabularies with varying text formats and styles, such as the presence of punctuation. Experimental results on various ASR tasks demonstrate that the proposed models improve over both the CTC and transducer-based baselines, owing to the incorporation of BERT knowledge. Moreover, our in-depth analysis and investigation verify the effectiveness of the proposed formulations and architectural designs.
Multi-Scale Temporal Transformer For Speech Emotion Recognition
Authors: Zhipeng Li, Xiaofen Xing, Yuanbo Fang, Weibin Zhang, Hengsheng Fan, Xiangmin Xu
Date: 2024-10-01 00:00:00
Tasks: Emotion Recognition, Speech Emotion Recognition

Speech emotion recognition plays a crucial role in human-machine interaction systems. Recently various optimized Transformers have been successfully applied to speech emotion recognition. However, the existing Transformer architectures focus more on global information and require large computation. On the other hand, abundant speech emotional representations exist locally on different parts of the input speech. To tackle these problems, we propose a Multi-Scale TRansfomer (MSTR) for speech emotion recognition. It comprises of three main components: (1) a multi-scale temporal feature operator, (2) a fractal self-attention module, and (3) a scale mixer module. These three components can effectively enhance the transformer's ability to learn multi-scale local emotion representations. Experimental results demonstrate that the proposed MSTR model significantly outperforms a vanilla Transformer and other state-of-the-art methods across three speech emotion datasets: IEMOCAP, MELD and, CREMAD. In addition, it can greatly reduce the computational cost.