Comparison of Current Frame-Based Phoneme Classifiers

Frame-by-frame Phoneme Classification Using Mlp

In this paper, we present some practical experiments for continuous speech frame-by-frame phoneme classification using Multi Layer Perceptron (MLP) neural networks. We used to train and test our software application, the the OASIS Numbers speech database. In our experiments, we tried to classify all the existing 32 phonemes together, from OASIS Numbers database dictionary. We also used differen...

Phoneme dependent frame selection preference

In previous study we proposed algorithms to select representative frames from a segment for phoneme likelihood evaluation. In this paper we show that this frame selection behavior is phoneme dependent. We observe that some phonemes benefit from frame selection while others do not, and that this separation matches the phonetic categories. For those phonemes sensitive to frame selection, we find ...

Frame-level phoneme classification using inductive inference

Robustness of Phoneme Classification Using Generative Classifiers: Comparison of the Acoustic Waveform and Plp Representations

The robustness of classification of isolated phoneme segments using generative classifiers is investigated for the acoustic waveform and PLP speech representations. Probabilistic PCA is used to fit a density to each phoneme class followed by maximum likelihood classification. The results show that although PLP performs best in quiet conditions, as the SNR decreases below 0dB acoustic waveforms ...

Single frame selection for phoneme classification

Our former study [1] has shown that maximum likelihood (ML) based frame selection, which selects reliable frames from a high resolution along the time axis, helps to improve the discrimination between phonemes. In this paper, we present our recent research on single frame selection for a phoneme classification task. A new single selection, which only selects one frame for one state in an Hidden...