Publication

An End-to-End Networks to Synthetize Intonation using a Generalized Command Response Model

Hervé Bourlard, Philip Neil Garner, Bastian Schnell, François Thierry M Marelli
2019
Conference Papers

Abstract

The generalized command response (GCR) model represents intonation as a superposition of muscle responses to spike command signals. We have previously shown that the spikes can be predicted by a two-stage system, consisting of a recurrent neural network and a post-processing procedure, but the responses themselves were fixed dictionary atoms. We propose an end-to-end neural architecture that replaces the dictionary atoms with trainable second-order recurrent elements analogous to recursive filters. We demonstrate gradient stability under modest conditions, and show that the system can be trained by imposing temporal sparsity constraints. Subjective listening tests demonstrate that the system can synthesize intonation with high naturalness, comparable to state-of-the-art acoustic models, and retains the physiological plausibility of the GCR model.

Official source

https://infoscience.epfl.ch/entities/publication/0dc2fa7c-2766-403b-8825-efb2b7b6d6e7

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Hervé Bourlard, Philip Neil Garner, Bastian Schnell, François Thierry M Marelli
2019
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/0dc2fa7c-2766-403b-8825-efb2b7b6d6e7

About this result

Related concepts (25)

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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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.

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