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 paper, we investigate multitask learning (MTL) approach for joint estimation of articulatory features with and without phoneme classification as subtask. Our studies show th...

This paper investigates the usefulness of segmental phonemedynamics for classification of speaking styles. We modeled transition details based on the phoneme sequences emitted by a speech recognizer, using data obtained from a recording of 39 depressed patients with 7 different speaking styles normal, pressured, slurred, stuttered, flat, slow and fast speech. We designed and compared two set of...

Speech recognition has gradually improved over the years, phoneme recognition in particular. Phoneme recognition plays very important role in speech processing. Phoneme strings are basic representation for automatic language recognition and it is proved that language recognition results are highly correlated with phoneme recognition results. Nowadays, many recognizers are based on Artificial ne...

Previous evidence suggests that the structure of similarity neighbourhoods in the developing mental lexicon may differ from that of the fully developed lexicon. The similarity relationships used to organize words into neighbourhoods was investigated in 20 pre-school children (age 3;7 to 5;11) using a two alternative forced-choice classification task. Children classified the similarity of test w...

Phoneme classification performance is a critical factor for the successful implementation of speech recognition system. A mispronunciation Arabic short vowels or long can change meaning complete sentence. However, correctly distinguishing phonemes with in Quranic recitation (the Holy book Muslims) still challenging problem even state-of-the-art methods, where duration considered one important f...

Out-of-vocabulary (OOV) words are the most challenging problem in automatic speech recognition (ASR), especially for morphologically rich languages. Most end-to-end systems performed at word and character levels of a language. Amharic is poorly resourced but This paper proposes hybrid connectionist temporal classification with attention architecture syllabification algorithm system (AASR) using...

Time-Delay Neural Networks (TDNN) have been shown by Waibel et al. [1] to be a good method for the classification of dynamic speech sounds such as voiced stop consonants. In this paper we discuss key issues in the design and training of a TDNN, based on a Multi-Layer Perceptron (MLP), when used for classification of the sets of voiced stop consonants (/b/, /d/, and /g/) and unvoiced stop conson...

In this paper, we propose a segment-based non-parametric method of monophone recognition. We pre-segment the speech utterance into its underlying phonemes using a group-delay-based algorithm. Then, we apply the kNN/SASH phoneme classification technique to classify the hypothesized phonemes. Since phoneme boundaries are already known during the decoding, the search space is very limited and the ...