Concept

Perceptrons (book)

Related publications (69)

Towards End-to-End Speech Recognition

Standard automatic speech recognition (ASR) systems follow a divide and conquer approach to convert speech into text. Alternately, the end goal is achieved by a combination of sub-tasks, namely, feature extraction, acoustic modeling and sequence decoding, ...

EPFL2016

Non-volatile memory as hardware synapse in neuromorphic computing: A first look at reliability issues

Irem Boybat Kara, Carmelo Di Nolfo

A large-scale artificial neural network, a three-layer perceptron, is implemented using two phase-change memory (PCM) devices to encode the weight of each of 164,885 synapses. The PCM conductances are programmed using a crossbar-compatible pulse scheme, an ...

IEEE2015

Experimental Demonstration and Tolerancing of a Large-Scale Neural Network (165 000 Synapses) Using Phase-Change Memory as the Synaptic Weight Element

Irem Boybat Kara, Severin Sidler, Carmelo Di Nolfo

Using two phase-change memory devices per synapse, a three-layer perceptron network with 164 885 synapses is trained on a subset (5000 examples) of the MNIST database of handwritten digits using a backpropagation variant suitable for nonvolatile memory (NV ...

Ieee-Inst Electrical Electronics Engineers Inc2015

Application of Artificial Neural Networks in Assessing the Equilibrium Depth of Local Scour Around Bridge Piers

Philippe Müllhaupt, Ehsan Sarshari

Scour can have the effect of subsidence of the piers in bridges, which can ultimately lead to the total collapse of these systems. Effective bridge design needs appropriate information on the equilibrium depth of local scour. The flow field around bridge p ...

2015

SVR vs MLP for Phone Duration Modelling in HMM-based Speech Synthesis

Philip Neil Garner, Pierre-Edouard Jean Charles Honnet

In this paper we investigate external phone duration models (PDMs) for improving the quality of synthetic speech in hidden Markov model (HMM)-based speech synthesis. Support Vector Regression (SVR) and Multilayer Perceptron (MLP) were used for this task. S ...

Idiap2014

SVR vs MLP for Phone Duration Modelling in HMM-based Speech Synthesis

Philip Neil Garner, Pierre-Edouard Jean Charles Honnet

2014

Estimating Phoneme Class Conditional Probabilities from Raw Speech Signal using Convolutional Neural Networks

Ronan Collobert, Dimitri Palaz

In hybrid hidden Markov model/artificial neural networks (HMM/ANN) automatic speech recognition (ASR) system, the phoneme class conditional probabilities are estimated by first extracting acoustic features from the speech signal based on prior knowledge su ...

2013

Estimating Phoneme Class Conditional Probabilities from Raw Speech Signal using Convolutional Neural Networks

Ronan Collobert, Dimitri Palaz

Idiap2013

Baseline System for Automatic Speech Recognition with French GlobalPhone Database

Milos Cernak, Sandrine Revaz

This report presents one month trainee work on development of French Automatic Speech Recognition ASR system using a french part of multilingual database GlobalPhone_FR. The purpose of this report is to explain and give results of the training and testing ...

Idiap2012

Transcribing Mandarin Broadcast Speech Using Multi-Layer Perceptron Acoustic Features

Fabio Valente, Wen Wang

Recently, several multi-layer perceptron (MLP)- based front-ends have been developed and used for Mandarin speech recognition, often showing significant complementary properties to conventional spectral features. Although widely used in multiple Mandarin s ...

2011

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.