Encoder-Decoder LSTM Machine Translation Model Overview This repository contains the implementation of a machine translation model using Encoder-Decoder architecture with Long Short-Term Memory (LSTM) ...

We will build a LSTM encoder-decoder using PyTorch to make sequence-to-sequence predictions for time series data. For illustrative purposes, we will apply our model to a synthetic time series dataset.

The time-series data is a type of sequential data and encoder-decoder models are very good with the sequential data and the reason behind this capability is the LSTM or RNN layer in the network.

Bone-conducted (BC) speech can be used to communicate in a very high noise environment. In this paper, a method of improving the quality of BC speech is presented. The speech signal of a speaker is ...

K Cho et al, ‘Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation’, arXiv.org. Keras tutorial on ‘Sequence to sequence example in Keras (character-level)’.

First, let us understand why an Attention Mechanism made machine translation easy. Previously encoder-decoder models were used for machine translation. The encoder-decoder model contains two networks ...

The speech signal of a speaker is passed through a novel dictionary representation-based encoder-decoder model. In the encoder, our designed non-negative and sparse long short-term memory (LSTM) ...