arXiv research

GE2E-AC improves accent classification by focusing on accent embeddings.

This paper describes the data collection effort that is part of the project Sprekend Nederland (The Netherlands Talking), and discusses its potential use in Automatic Accent Location. We define Automatic Accent Location as the task to describe the accent of a speaker in terms of the location of the speaker and its hist…

MPSA-DenseNet improves accent classification accuracy.

State-of-the-art automatic speech recognition (ASR) systems struggle with the lack of data for rare accents. For sufficiently large datasets, neural engines tend to outshine statistical models in most natural language processing problems. However, a speech accent remains a challenge for both approaches. Phonologists ma…

End-to-end speech synthesis is a promising approach that directly converts raw text to speech. Although it was shown that Tacotron2 outperforms classical pipeline systems with regards to naturalness in English, its applicability to other languages is still unknown. Japanese could be one of the most difficult languages …

The aim of this article is to present the category of bounded Frechet manifolds in respect to which we will review the geometry of Frechet manifolds with a stronger accent on its metric aspect. An inverse function theorem in the sense of Nash and Moser in this category is proved, and some applications to Riemannian geo…

NLE embeds labels for domain adaptation with neural networks.

Unified analysis of efficient local training methods for distributed variational inequalities.

Active learning reduces SP calculations by 90%.

In this work, we argue that the implications of Pseudo and Quantum Random Number Generators (PRNG and QRNG) inexplicably affect the performances and behaviours of various machine learning models that require a random input. These implications are yet to be explored in Soft Computing until this work. We use a CPU and a …

Spoken language identification (LID) technologies have improved in recent years from discriminating largely distinct languages to discriminating highly similar languages or even dialects of the same language. One aspect that has been mostly neglected, however, is discrimination of languages for multilingual speakers, d…

We investigated the impact of noisy linguistic features on the performance of a Japanese speech synthesis system based on neural network that uses WaveNet vocoder. We compared an ideal system that uses manually corrected linguistic features including phoneme and prosodic information in training and test sets against a …

Paper proposes a new framework for SAD using GANs.

CL methods improve monolingual ASR models across new tasks without forgetting past data.

This paper proposes a voice conversion (VC) method using sequence-to-sequence (seq2seq or S2S) learning, which flexibly converts not only the voice characteristics but also the pitch contour and duration of input speech. The proposed method, called ConvS2S-VC, has three key features. First, it uses a model with a fully…

Embeddings are ubiquitous in machine learning, appearing in recommender systems, NLP, and many other applications. Researchers and developers often need to explore the properties of a specific embedding, and one way to analyze embeddings is to visualize them. We present the Embedding Projector, a tool for interactive v…

Proposes QQE for transforming and embedding data distributions.

Maps can be embedded in higher dimensions if they lift to embeddings in product spaces.

New embeddings for manifolds using heat kernels.

Introduces PELP for graph-enhanced word embeddings.

Curvature regularization prevents distortion in graph embeddings.

Word embeddings are a powerful approach for unsupervised analysis of language. Recently, Rudolph et al. (2016) developed exponential family embeddings, which cast word embeddings in a probabilistic framework. Here, we develop dynamic embeddings, building on exponential family embeddings to capture how the meanings of w…

Proposes cone embedding for better graph hierarchical structure representation.

Classifies linear embeddings of grassmannians and ind-grassmannians.

BC-Aligner maintains backward compatibility of embeddings after frequent updates.

Embedding calculus proves convergence for surfaces.

Unified framework for word embedding models using noise examples.

Models use embeddings and attention for better claim severity prediction.

A fast graph embedding method for large graphs.

Paper proves impossibility of three desirable properties in node embedding.

The study characterizes and verifies equivariant embeddings of symmetric Kählerian manifolds.

Proves uniqueness of embedding complex manifold into infinite-dimensional space.

The paper defines invariants for almost graph embeddings and explores their properties.

Recently, click-through rate (CTR) prediction models have evolved from shallow methods to deep neural networks. Most deep CTR models follow an Embedding\&MLP paradigm, that is, first mapping discrete id features, e.g. user visited items, into low dimensional vectors with an embedding module, then learn a multi-layer pe…

This work analyzes PPR-based node embeddings and their topological information.

Network representation learning in low dimensional vector space has attracted considerable attention in both academic and industrial domains. Most real-world networks are dynamic with addition/deletion of nodes and edges. The existing graph embedding methods are designed for static networks and they cannot capture evol…

For leveled spatial graphs, we find a surface embedding that allows cellular embedding.

Long spacelike embeddings can be approximated by isometric ones.

A {\it wrinkled embedding} $f:V^n\to W^m$ is a topological embedding which is a smooth embedding everywhere on $V$ except a set of $(n-1)$-dimensional spheres, where $f$ has cuspidal corners. In this paper we prove that any rotation of the tangent plane field $TV\subset TW$ of a {\it smoothly embedded} submanifold $V\s…

Graph embeddings have become a key and widely used technique within the field of graph mining, proving to be successful across a broad range of domains including social, citation, transportation and biological. Graph embedding techniques aim to automatically create a low-dimensional representation of a given graph, whi…

Graphs embeddable on torus and linklessly in 3D can be embedded linklessly in standard torus.

Geometrically transforms word embeddings into a common space for better comparison.

Obtaining continuous representations of structural data such as directed acyclic graphs (DAGs) has gained attention in machine learning and artificial intelligence. However, embedding complex DAGs in which both ancestors and descendants of nodes are exponentially increasing is difficult. Tackling in this problem, we de…

The paper proves nonexistence and existence results for minimal surfaces in R^4.

Recent works reveal that network embedding techniques enable many machine learning models to handle diverse downstream tasks on graph structured data. However, as previous methods usually focus on learning embeddings for a single network, they can not learn representations transferable on multiple networks. Hence, it i…

MCE reduces embedding instability in nonlinear dimensionality reduction.

Natural language processing has improved tremendously after the success of word embedding techniques such as word2vec. Recently, the same idea has been applied on source code with encouraging results. In this survey, we aim to collect and discuss the usage of word embedding techniques on programs and source code. The a…

Paper uses JIVE to decompose word embeddings, improving sentiment analysis performance.