Flexible SC framework converts voices from non-aligned corpora.
problem Limited practical applications of SC due to lack of parallel corpora.
method Variational auto-encoder framework for non-parallel corpora.
result Framework enables spectral conversion without parallel corpora or alignments.
Voice conversion methods degrade in noisy conditions, with BLFWAS outperforming others.
problem Robustness of voice conversion techniques under mismatched conditions.
method Comparative analysis of five VC techniques on CMU ARCTIC corpus, exploring speech enhancement techniques.
result Bilinear frequency warping with amplitude scaling (BLFWAS) outperforms other methods in noisy conditions.
Proposes a method to update dictionaries for NMF-based voice conversion.
problem Difficulty in obtaining a small and effective dictionary for NMF-based SC systems.
method Uses an encoder-decoder network reformulation to update NMF dictionaries.
result Significant gains in system performance with smaller dictionaries.
Covering spectra match if the covering is amenable, with conditions on curvature.
problem Matching spectra of Riemannian coverings under amenability conditions.
method Analyzing spectra of Riemannian manifolds and their coverings under completeness and curvature constraints.
result Spectra match if the covering is amenable, with conditions on curvature.
Enhanced transformer converts whispered speech to natural speech.
problem Machine recognition of whispered speech is challenging.
method Proposes an enhanced transformer architecture trained end-to-end using supervised learning.
result Similar formant distributions of converted speech to groundtruth.
Paper proposes ACVAE-VC for non-parallel voice conversion.
problem Non-parallel many-to-many voice conversion with attribute class label retention.
method Uses ACVAE with fully convolutional networks, information-theoretic regularization, and auxiliary classifier.
result Successfully retains attribute class labels and avoids buzzy speech.
The paper extends spectral estimates to hyperbolic surfaces with hyperbolic ends.
problem Proving a necessary condition for observability of the heat semigroup on manifolds.
method Propagation of smallness estimates of Carleman and Logunov-Malinnikova type.
result Established spectral estimates for surfaces with hyperbolic ends, proving the thickness condition is necessary.
The paper studies spectral analysis on complex spaces and finds explicit formulas for eigensections.
problem Understanding eigensections on complex projective spaces and Grassmannians.
method Using creation and annihilation operators, converting higher energy eigensections to lower energy holomorphic sections.
result Explicit formulas for the dimension of higher-level eigensections on Pn. We present a definition of Riemannian manifold in noncommutative geometry. Using products of unbounded Kasparov modules, we show one can obtain such Riemannian manifolds from noncommutative spin^c manifolds; and conversely, in the presence of a spin^c structure. We also show how to obtain an analogue of Kasparov's fund…
Method converts speech with attention and context preservation.
problem Voice conversion with improved stability and efficiency.
method Sequence-to-Sequence learning with attention and context preservation.
result Synthesized speech quality comparable to advanced methods.
AI agents on social networks rarely engage in extended conversations.
problem Understanding the persistence of interactions in AI-agent social networks.
method Analysis of Moltbook, a social network of AI agents, using interaction half-life and spectral tests.
result Most comments on Moltbook receive a direct reply within seconds, indicating a ``fast response or silence'' regime.
This note is devoted to Keller-Lieb-Thirring spectral estimates for Schrödinger operators on infinite cylinders: the absolute value of the ground state level is bounded by a function of a norm of the potential. Optimal potentials with small norms are shown to depend on a single variable. The proof is a perturbation arg…
In this paper we establish a duality between etale Lie groupoids and a class of non-necessarily commutative algebras with a Hopf algebroid structure. For any etale Lie groupoid G over a manifold M, the groupoid algebra C_c(G) of smooth functions with compact support on G has a natural coalgebra structure over C_c(M) wh…
The paper proves geometric and spectral alignment for deep neural networks.
problem Understanding the singular spectra of deep neural network layers.
method Proves deterministic quotient-geometric estimates for singular spectra of Frobenius-normalized layer factors.
result Exact power-law spectra form a trace-normalized Cartan orbit under Frobenius normalization.
Let (M,g) be a complete non-compact Riemannian manifold. We consider operators of the form Δg+V, where Δg is the non-negative Laplacian associated with the metric g, and V a locally integrable function. Let ρ:(M^,g^)→(M,g) be a Riemannian covering, with Laplacian Δg^ and potenti…
Paper improves conversion prediction models for online advertising.
problem Predicting different types of conversions in online advertising.
method Multi-Task Learning with MT-FwFM.
result Improved AUC by 0.74% and 0.84% on two conversion types, and overall AUC by 0.50%.
Study examines cash conversion cycle in manufacturing firms, finding negative relationships with profitability and size.
problem Understanding cash conversion cycle in manufacturing firms and its impact on profitability and size.
method Empirical study of 30 manufacturing firms in Dhaka Stock Exchanges, categorizing them into six industries, analyzing industry averages and relationships with size and profitability.
result Negative relationship between cash conversion cycle and profitability, especially ROE; negative relationship with firm size in terms of net sales.
Framework converts singer identity and vocal technique from non-parallel corpora.
problem Converts singer identity and vocal technique from non-parallel corpora.
method Uses variational autoencoders with separate encoders for singer identity and vocal technique.
result Successfully disentangles and converts singer identity and vocal technique.
FasterVoiceGrad speeds up VC by 6-7x with novel distillation.
problem Slow iterative sampling in diffusion-based VC models.
method Adversarial diffusion conversion distillation (ADCD) to create a faster one-step model.
result 6.6-6.9 and 1.8x faster on GPU and CPU, respectively.
Hyperspectral remote sensing images (HSIs) usually have high spectral resolution and low spatial resolution. Conversely, multispectral images (MSIs) usually have low spectral and high spatial resolutions. The problem of inferring images which combine the high spectral and high spatial resolutions of HSIs and MSIs, resp…
Efficiently estimates conversion probability in online display advertising.
problem Estimating conversion probability with delays and large data sets.
method Compromise estimator combining logistic regression and joint model.
result Computational efficiency with less bias than previous methods.
Conversational UCB accelerates bandit learning with user feedback.
problem Slow learning speed in traditional contextual bandit algorithms.
method Generalized contextual bandit to conversational contextual bandit, leveraging both behavioral and conversational feedbacks.
result ConUCB achieves a smaller regret upper bound, indicating faster learning speed.
Automated system extracts medication regimens from medical conversations.
problem Extract relevant medication information from medical conversations.
method QA task approach, combined QA and Information Extraction, data augmentation, public embeddings, pretraining.
result Improved accuracy in extracting dosage and frequency from 54.28 and 37.13 to 89.57 and 45.94.
Automated speaker fluency level assessment using machine learning.
problem Time-consuming manual evaluation of non-native English speakers' fluency levels.
method Built a dataset of labeled audio conversations, extracted features, and trained machine learning models.
result Support vector machine achieved 94.39% classification accuracy.
Improved speech recognition for medical conversations.
problem Transcribing accurate doctor-patient conversations.
method Collected a large dataset, designed realistic tasks, explored alignment and model types (CTC and LAS).
result LAS models were more resilient to noisy data, while CTC required more data cleanup.
FedConPE improves conversational recommender systems efficiency and privacy.
problem Efficiently eliciting user preferences in interactive systems with heterogeneous clients.
method Phase elimination-based federated conversational bandit algorithm with adaptive key term construction.
result Minimizes uncertainty across all dimensions in feature space and offers improved efficiency and privacy.
The classical setting of community detection consists of networks exhibiting a clustered structure. To more accurately model real systems we consider a class of networks (i) whose edges may carry labels and (ii) which may lack a clustered structure. Specifically we assume that nodes possess latent attributes drawn from…
Solution for voice conversion with limited data using hierarchical seq2seq and attention models.
problem Voice conversion between speakers with limited parallel audio pairs.
method Hierarchical sequence to sequence model with attention-based decoder, trained on single speaker dataset.
result Improved voice conversion quality using mel spectrograms and wavenet vocoder.
Proposes a multi-state model for evaluating life insurance conversion options.
problem Evaluating the value of conversion options in life insurance contracts.
method Age-indexed semi-Markov chains to model duration, time non-homogeneity, and ageing effects.
result Validates the model's ability to accurately evaluate conversion option values.
Study proposes a time-aware model to predict user conversion intent.
problem Weak predictive signals from users not suitable for conversion prediction.
method Time-aware approach to model user activities and capture conversion intent signals.
result Approach outperforms other models on real-world datasets.
Improved voice conversion with semi-supervised learning.
problem Voice conversion with limited parallel data.
method Amortized variational inference with parallel and non-parallel utterances.
result Semi-supervised training improves voice conversion performance.
Improved SAR in asynchronous conversations using neural models and unlabeled data.
problem Lack of labeled data for SAR in asynchronous conversations.
method Hierarchical LSTM-CRF model, semi-supervised learning with word embeddings, adversarial training.
result Adversarial training improves SAR performance by leveraging labeled data from synchronous domains.
CycleGAN-VC3 improves CycleGAN-VCs for mel-spectrogram conversion.
problem Ambiguity in CycleGAN-VC/VC2 effectiveness for mel-spectrogram conversion.
method Proposes CycleGAN-VC3 with time-frequency adaptive normalization (TFAN).
result CycleGAN-VC3 outperforms or matches CycleGAN-VC2 for mel-spectrogram conversion.
ConvS2S-VC converts voice characteristics and pitch contour using a fully convolutional seq2seq model.
problem Voice conversion with preservation of pitch contour and duration.
method Fully convolutional seq2seq architecture with conditional batch normalization.
result ConvS2S-VC outperforms baseline methods in sound quality and speaker similarity.
A new algorithm for conversational recommendation systems using dueling bandits in GLMs.
problem Limited user feedback in existing conversational bandit methods.
method Integrates dueling bandits with relative feedback in generalized linear models.
result Theoretical and empirical validation of ConDuel's efficacy.
This paper improves OMP-based sparse subspace clustering with data-adaptive capability.
problem Existing OMP-based approaches lack data adaptiveness, leading to inaccurate data representation.
method Develops a parameter selection process to adjust OMP parameters based on data distribution and introduces a new SEA ratio metric.
result Proposed approach achieves better clustering accuracy, SEA ratio, and representation quality compared to other OMP-based methods.
Develops a new representation for constant mean curvature surfaces in hyperbolic 3-space.
problem Finding conformal immersions of constant mean curvature in hyperbolic 3-space.
method Uses a Weierstrass-Kenmotsu type representation based on the Hermitian model, balanced spectral deformation, and Iwasawa splitting of $\SL$.
result Establishes an explicit correspondence with Aiyama and Akutagawa's representation and interprets the construction in terms of Kokubu's adjusted normal Gauss map.
The paper extracts structured data from physician-patient conversations, reducing clerical burden.
problem Mining insights from physician-patient conversations for electronic health record documentation.
method Created a dataset of transcripts and summaries, extracted noteworthy utterances, and improved model performance.
result Extracting noteworthy utterances significantly boosts model performance for recognizing diagnoses and RoS abnormalities.
This paper analyzes AJIVE for estimating shared subspace across multiple datasets, revealing its strengths and limitations.
problem Estimating shared subspace across multiple datasets with varying degrees of misalignment.
method Angle-based Joint and Individual Variation Explained (AJIVE) method, a two-stage spectral approach.
result AJIVE's performance in high signal-to-noise ratio (SNR) regimes and its non-diminishing error in low-SNR settings.
Schedule-free SGD is optimal for nonconvex optimization problems.
problem Nonconvex optimization in neural networks.
method Developed a general framework for online-to-nonconvex conversion, which converts schedule-free SGD into an effective nonconvex optimization algorithm.
result Schedule-free SGD achieves optimal iteration complexity for nonsmooth, nonconvex optimization problems.
Language models fail to process hallucinated responses, and this study diagnoses the failure.
problem Language models fail to process hallucinated responses, leading to over-concentration or diffuse attention.
method The study uses forced scoring of benchmark-labeled responses to compute attention shapes and analyze the symmetric component of the degree-normalized attention operator.
result The study proves that every transpose-invariant spectral diagnostic of the attention operator is orientation-blind and bounds the sensitivity of any Lipschitz diagnostic by the asymmetry coefficient \(G\).
Improved autoencoder for F0-consistent voice conversion.
problem Non-parallel many-to-many voice conversion with prosodic information leakage.
method Conditional autoencoder with information-constraining bottlenecks.
result Controlled F0 contour and improved speech quality.
Improved conversion rate prediction in online advertising using self-supervised pre-training.
problem Data sparsity and calibration issues in predicting conversions given clicks.
method Self-supervised pre-training on all conversion events to enrich CVR prediction model without compromising calibration.
result Improvements in offline training and online A/B tests, with full deployment to Yahoo native advertising system.
A fast voice conversion method using diffusion models.
problem One-shot many-to-many voice conversion.
method Diffusion probabilistic modeling with Fast Maximum Likelihood Sampling Scheme.
result Superior quality compared to state-of-the-art approaches.
A method corrects feedback shift in predicting conversion rates with delayed feedback.
problem Delayed feedback leads to mislabeling of positive instances in training data.
method Uses importance weight approach to correct feedback shift.
result Proposed method outperforms existing methods in offline and online experiments.
Proposes a new voice conversion model that preserves pitch patterns.
problem Preserving pitch patterns while changing speaker identity.
method Variational-autoencoder-based model with an auxiliary network.
result Ensures the conversion result correctly reflects specified F0/timbre information.
New method detects business-relevant outliers in e-commerce conversion rates.
problem Identifying outliers in e-commerce conversion rate data.
method A novel unsupervised fluid IQR method that adjusts sensitivity based on platform activity.
result Fluid IQR method outperforms existing methods in business-relevance and robustness.
The paper provides a converse to linking theorems for graphs in 3-space and higher dimensions.
problem Linking properties of graphs in 3-space and higher dimensions.
method Proves a converse to specific linking theorems for graphs in 3-space and higher dimensions.
result Proves a higher-dimensional analogue of a converse to a lemma by Segal-Spież.