4 research outputs found
Semi-tied Units for Efficient Gating in LSTM and Highway Networks
Gating is a key technique used for integrating information from multiple
sources by long short-term memory (LSTM) models and has recently also been
applied to other models such as the highway network. Although gating is
powerful, it is rather expensive in terms of both computation and storage as
each gating unit uses a separate full weight matrix. This issue can be severe
since several gates can be used together in e.g. an LSTM cell. This paper
proposes a semi-tied unit (STU) approach to solve this efficiency issue, which
uses one shared weight matrix to replace those in all the units in the same
layer. The approach is termed "semi-tied" since extra parameters are used to
separately scale each of the shared output values. These extra scaling factors
are associated with the network activation functions and result in the use of
parameterised sigmoid, hyperbolic tangent, and rectified linear unit functions.
Speech recognition experiments using British English multi-genre broadcast data
showed that using STUs can reduce the calculation and storage cost by a factor
of three for highway networks and four for LSTMs, while giving similar word
error rates to the original models.Comment: To appear in Proc. INTERSPEECH 2018, September 2-6, 2018, Hyderabad,
Indi
DNN speaker adaptation using parameterised sigmoid and ReLU hidden activation functions
This paper investigates the use of parameterised sigmoid and rectified linear unit (ReLU) hidden activation functions in deep neural network (DNN) speaker adaptation. The sigmoid and ReLU parameterisation schemes from a previous study for speaker independent (SI) training are used. An adaptive linear factor associated with each sigmoid or ReLU hidden unit is used to scale the unit output value and create a speaker dependent (SD) model. Hence, DNN adaptation becomes re-weighting the importance of different hidden units for every speaker. This adaptation scheme is applied to both hybrid DNN acoustic modelling and DNN-based bottleneck (BN) feature extraction. Experiments using multi-genre British English television broadcast data show that the technique is effective in both directly adapting DNN acoustic models and the BN features, and combines well with other DNN adaptation techniques. Reductions in word error rate are consistently obtained using parameterised sigmoid and ReLU activation function for multiple hidden layer adaptation
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Supplementary data for "DNN Speaker Adaptation using Parameterised Sigmoid and ReLU Hidden Activation Functions"
Description of the Speech Recognition Training and Test Data and its Availability used for Experiments. Key Speech Recognition Outputs/Detailed Scoring Results used in the paper.This work was supported by the EPSRC [grant number EP/I031022/1] and by Cambridge Commonwealth, European & International Trust
Applications of artificial neural networks in three agro-environmental systems: microalgae production, nutritional characterization of soils and meteorological variables management
La agricultura es una actividad esencial para los humanos, es altamente dependiente de las condiciones meteorol贸gicas y foco de investigaci贸n e innovaci贸n con el objetivo de enfrentar diversos desaf铆os. El cambio clim谩tico, calentamiento global y la degradaci贸n de los ecosistemas agr铆colas son s贸lo algunos de los problemas que los humanos enfrentamos para continuar con la esencial producci贸n de alimentos. Buscando la innovaci贸n en el sector agr铆cola, se consideraron tres t贸picos principales de investigaci贸n para esta tesis; la producci贸n de microalgas, el color del suelo y la fertilidad, y la adquisici贸n de datos meteorol贸gicos. Estos temas tienen roles cada vez m谩s importantes en la agricultura, especialmente bajo la incertidumbre del futuro de la producci贸n de alimentos. Las microalgas son una interesante alternativa para la fertilizaci贸n de cultivos y la sostenibilidad del suelo; mientras que los par谩metros de fertilidad del suelo necesitan ser m谩s estudiados para desarrollar m茅todos de an谩lisis de menor costo y m谩s r谩pidos para ayudar al manejo. La agricultura, como actividad altamente dependiente del clima, necesita de datos meteorol贸gicos para anticipar eventos, planificar y manejar los cultivos eficientemente. Estos temas se seleccionaron con el prop贸sito de mejorar el estado actual de la t茅cnica, proponer nuevas alternativas basadas, principalmente, en la aplicaci贸n de redes neuronales artificiales (ANN) como una manera novedosa de resolver los problemas y generar conocimiento de aplicaci贸n directa en sistemas de cultivos. El objetivo principal de esta tesis fue generar modelos de ANNs capaces de abordar problemas relacionados con la agricultura, como una alternativa a los m茅todos tradicionales y m谩s costosos empleados en el manejo, an谩lisis y adquisici贸n de datos en los sistemas agrarios.Departamento de Ingenier铆a Agr铆cola y ForestalDoctorado en Ciencia e Ingenier铆a Agroalimentaria y de Biosistema