Publication

Subspace Regularized Dynamic Time Warping for Spoken Query Detection

2017
Conference Papers

Abstract

Deep neural network posterior probabilities are the best features for query detection in speech archives. Dynamic time warping (DTW) is the state-of-the-art solution for this task. Posterior features live in low-dimensional subspaces whereas, the current DTW methods do not incorporate this global structure of the data and rely on local feature distances. We exploit the query example as the dictionary for sparse recovery. Local DTW scores are integrated with the sparse reconstruction scores to obtain a subspace regularized distance matrix for DTW. The proposed method yields a substantial performance gain over the baseline system.

Official source

https://infoscience.epfl.ch/entities/publication/9a890a54-02fe-4311-9190-16987d4a4863

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2017
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/9a890a54-02fe-4311-9190-16987d4a4863

About this result

Related concepts (32)

Types of artificial neural networks

There are many types of artificial neural networks (ANN). Artificial neural networks are computational models inspired by biological neural networks, and are used to approximate functions that are generally unknown. Particularly, they are inspired by the behaviour of neurons and the electrical signals they convey between input (such as from the eyes or nerve endings in the hand), processing, and output from the brain (such as reacting to light, touch, or heat). The way neurons semantically communicate is an area of ongoing research.

Artificial neural network

Artificial neural networks (ANNs, also shortened to neural networks (NNs) or neural nets) are a branch of machine learning models that are built using principles of neuronal organization discovered by connectionism in the biological neural networks constituting animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons.

Dimensionality reduction

Dimensionality reduction, or dimension reduction, is the transformation of data from a high-dimensional space into a low-dimensional space so that the low-dimensional representation retains some meaningful properties of the original data, ideally close to its intrinsic dimension. Working in high-dimensional spaces can be undesirable for many reasons; raw data are often sparse as a consequence of the curse of dimensionality, and analyzing the data is usually computationally intractable (hard to control or deal with).

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Subspace Regularized Dynamic Time Warping for Spoken Query Detection

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