A hybrid ANN/DBN approach to articulatory feature recognition
نویسندگان
چکیده
Artificial neural networks (ANN) have proven to be well suited to the task of articulatory feature (AF) recognition. However, one drawback with an ANN approach is that features are assumed to be statistically independent. We address this by using ANNs to provide virtual evidence to a dynamic Bayesian network (DBN). This gives a hybrid ANN/DBN model and allows modelling of inter-feature dependencies. We demonstrate significant increases in AF recognition accuracy from modelling dependencies between features, and present the results of embedded training experiments in which a set of asynchronous feature changes are learned. Furthermore, we report on the application of a Viterbi training scheme in which we alternate between realigning the AF training labels and retraining the ANNs.
منابع مشابه
A Hybrid Approach Based on Higher Order Spectra for Clinical Recognition of Seizure and Epilepsy Using Brain Activity
Introduction: This paper proposes a reliable and efficient technique to recognize different epilepsy states, including healthy, interictal, and ictal states, using Electroencephalogram (EEG) signals. Methods: The proposed approach consists of pre-processing, feature extraction by higher order spectra, feature normalization, feature selection by genetic algorithm and ranking method, and classif...
متن کاملSpeech Attribute Detection Using Deep Learning
In this work we present alternative models for attribute speech feature extraction based on the two state-of-the-art deep neural networks: convolutional neural networks (CNN) and feed-forward neural network with pretraining using stack of restricted Boltzmann machines (DBN-DNN). These attribute detectors are trained using data-driven approach across all languages in the OGI-TS multi-language te...
متن کاملApplication of Pretrained Deep Neural Networks to Large Vocabulary Speech Recognition
The use of Deep Belief Networks (DBN) to pretrain Neural Networks has recently led to a resurgence in the use of Artificial Neural Network Hidden Markov Model (ANN/HMM) hybrid systems for Automatic Speech Recognition (ASR). In this paper we report results of a DBN-pretrained context-dependent ANN/HMM system trained on two datasets that are much larger than any reported previously with DBN-pretr...
متن کاملHidden feature models for speech recognition using dynamic Bayesian networks
In this paper, we investigate the use of dynamic Bayesian networks (DBNs) to explicitly represent models of hidden features, such as articulatory or other phonological features, for automatic speech recognition. In previous work using the idea of hidden features, the representation has typically been implicit, relying on a single hidden state to represent a combination of features. We present a...
متن کاملPhoto-realistic visual speech synthesis based on AAM features and an articulatory DBN model with constrained asynchrony
This paper presents a photo realistic visual speech synthesis method based on an audio visual articulatory dynamic Bayesian network model (AF_AVDBN) in which the maximum asynchronies between the articulatory features, such as lips, tongue and glottis/velum, can be controlled. Perceptual linear prediction (PLP) features from the audio speech and active appearance model (AAM) features from mouth ...
متن کامل