The role of automatic emotion recognition from speech is growing continuously because of the accepted importance of reacting o the emotional state of the user in human–computer interaction. Most state-of-the-art emotion recognition methods are based on urnand frame-level analysis independent from phonetic transcription. Here, we are interested in a phoneme-based classification f the level of ar...

Phoneme classification and recognition is the first step to large vocabulary continuous speech recognition. This step represents the acoustic modeling part of such a system. In hybrid speech recognition systems phoneme recognition is made by artificial neural networks (ANN’s). The main objective of this paper is the investigation of dynamic ANN’s, namely the Time-Delay Neural Networks (TDNN) an...

An acoustic-phonetic featureand knowledge-based system for the automatic segmentation, broad categorization and fine phoneme recognition of continuous speech is described. The system uses an auditory-based front-end processing and incorporates new knowledge-based algorithms to automatically segments the speech into phoneme-like segments that are further categorized into 4 main categories: sonor...

Phoneme classification is investigated for linear feature domains with the aim of improving robustness to additive noise. In linear feature domains noise adaptation is exact, potentially leading to more accurate classification than representations involving non-linear processing and dimensionality reduction. A generative framework is developed for isolated phoneme classification using linear fe...

Abstract. We present an algorithmic framework for phoneme classification where the set of phonemes is organized in a predefined hierarchical structure. This structure is encoded via a rooted tree which induces a metric over the set of phonemes. Our approach combines techniques from large margin kernel methods and Bayesian analysis. Extending the notion of large margin to hierarchical classifica...

In this paper we propose a k-NN/SASH phoneme classification algorithm that competes favourably with state-ofthe-art methods. We apply a similarity search algorithm (SASH) that has been used successfully for classification of high dimensional texts and images. Unlike other search algorithms, the computational time of SASH is not affected by the dimensionality of the data. Therefore, we generate ...

This paper addresses the problem of binary phoneme classification via a neural net segment-based approach. Phoneme groups are categorized based on articulatory information. For an efficient segmental acoustic properties capture, the phoneme associated with a speech segment is represented using MFCC’s features extracted from different portions of that segment as well as its duration. These porti...

This paper describes a proposed automatic language accent identification system based on phoneme class trajectory models. Our focus is to preserve discriminant information of the spectral evolution that belong to each accent. Here, we describe two classification schemes based on stochastic trajectory models; supervised and unsupervised classification. For supervised classification, we assume te...

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 ...