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CSL
2016
Springer

Integrating articulatory data in deep neural network-based acoustic modeling

4 years 3 months ago
Integrating articulatory data in deep neural network-based acoustic modeling
Hybrid deep neural network–hidden Markov model (DNN-HMM) systems have become the state-of-the-art in automatic speech recognition. In this paper we experiment with DNN-HMM phone recognition systems that use measured articulatory information. Deep neural networks are both used to compute phone posterior probabilities and to perform acoustic-to-articulatory mapping (AAM). The AAM processes we propose are based on deep representations of the acoustic and the articulatory domains. Such representations allow to: (i) create different pre-training configurations of the DNNs that perform AAM; (ii) perform AAM on a transformed (through DNN autoencoders) articulatory feature (AF) space that captures strong statistical dependencies between articulators. Traditionally, neural networks that approximate the AAM are used to generate AFs that are appended to the observation vector of the speech recognition system. Here we also study a novel approach (AAM-based pretraining) where a DNN performing t...
Leonardo Badino, Claudia Canevari, Luciano Fadiga,
Added 01 Apr 2016
Updated 01 Apr 2016
Type Journal
Year 2016
Where CSL
Authors Leonardo Badino, Claudia Canevari, Luciano Fadiga, Giorgio Metta
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