Improving Articulatory Feature and Phoneme Recognition using Multitask Learning
Publications associées (49)
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Speech sounds can be characterized by articulatory features. Articulatory features are typically estimated using a set of multilayer perceptrons (MLPs), i.e., a separate MLP is trained for each articulatory feature. In this report, we investigate multitask ...
One of the main challenge in non-native speech recognition is how to handle acoustic variability present in multiaccented non-native speech with limited amount of training data. In this paper, we investigate an approach that addresses this challenge by usi ...
Using phone posterior probabilities has been increasingly explored for improving automatic speech recognition (ASR) systems. In this paper, we propose two approaches for hierarchically enhancing these phone posteriors, by integrating long acoustic context, ...
In this thesis, we investigate a hierarchical approach for estimating the phonetic class-conditional probabilities using a multilayer perceptron (MLP) neural network. The architecture consists of two MLP classifiers in cascade. The first MLP is trained in ...
This paper proposes a novel grapheme-to-phoneme (G2P) conversion approach where first the probabilistic relation between graphemes and phonemes is captured from acoustic data using Kullback-Leibler divergence based hidden Markov model (KL-HMM) system. Then ...
We describe a kernel wrapper, a Mercer kernel for the task of phoneme sequence recognition which is based on operations with the Gaussian kernel, and suitable for any sequence kernel classifier. We start by presenting a kernel-based algorithm for phoneme s ...
In a recent work, we proposed an acoustic data-driven grapheme-to-phoneme (G2P) conversion approach, where the probabilistic relationship between graphemes and phonemes learned through acoustic data is used along with the orthographic transcription of word ...
This paper proposes a novel grapheme-to-phoneme (G2P) conversion approach where first the probabilistic relation between graphemes and phonemes is captured from acoustic data using Kullback-Leibler divergence based hidden Markov model (KL-HMM) system. Then ...
In this paper, we propose a simple approach to jointly model both grapheme and phoneme information using Kullback-Leibler divergence based HMM (KL-HMM) system. More specifically, graphemes are used as subword units and phoneme posterior probabilities estim ...
Idiap2009
In this work, we investigate the possible use of k-nearest neighbour (kNN) classifiers to perform frame-based acoustic phonetic classification, hence replacing Gaussian Mixture Models (GMM) or MultiLayer Perceptrons (MLP) used in standard Hidden Markov Mod ...
