Publication

Hierarchical Neural Networks Feature Extraction for LVCSR system

2007
Conference Papers

Abstract

This paper investigates the use of a hierarchy of Neural Networks for performing data driven feature extraction. Two different hierarchical structures based on long and short temporal context are considered. Features are tested on two different LVCSR systems for Meetings data (RT05 evaluation data) and for Arabic Broadcast News (BNAT05 evaluation data). The hierarchical NNs outperforms the single NN features consistently on different type of data and tasks and provides significant improvements w.r.t. respective baselines systems. Best result is obtained when different time resolutions are used at different level of the hierarchy.

Official source

https://infoscience.epfl.ch/entities/publication/dbb63fad-a645-463d-a66a-604b373a490d

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2007
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/dbb63fad-a645-463d-a66a-604b373a490d

About this result

Related concepts (25)

Data

In common usage and statistics, data (USˈdætə; UKˈdeɪtə) is a collection of discrete or continuous values that convey information, describing the quantity, quality, fact, statistics, other basic units of meaning, or simply sequences of symbols that may be further interpreted formally. A datum is an individual value in a collection of data. Data is usually organized into structures such as tables that provide additional context and meaning, and which may themselves be used as data in larger structures.

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.

Recurrent neural network

A recurrent neural network (RNN) is one of the two broad types of artificial neural network, characterized by direction of the flow of information between its layers. In contrast to uni-directional feedforward neural network, it is a bi-directional artificial neural network, meaning that it allows the output from some nodes to affect subsequent input to the same nodes. Their ability to use internal state (memory) to process arbitrary sequences of inputs makes them applicable to tasks such as unsegmented, connected handwriting recognition or speech recognition.

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