The Support Vector Machine (SVM)method has been widely used in numerous classification tasks. The main idea of this algorithm is based on the principle of the margin maximization to find an hyperplane which separates the data into two different classes.In this paper, SVM is applied to phoneme recognition task. However, in many real-world problems, each phoneme in the data set for recognition pr...

This paper presents an experiment in speech recognition whereby multiple phoneme recognisers are applied to the same utterance. When these recognisers agree on an hypothesis for the same time interval, that hypothesis is assumed to be correct. When they are in disagreement, fine-grained phonetic features, called articulatory features, recognised from the same speech utterance are used to create...

This paper presents an ESN-based Arabic phoneme recognition system trained with supervised, forced and combined supervised/forced supervised learning algorithms. Mel-Frequency Cepstrum Coefficients (MFCCs) and Linear Predictive Code (LPC) techniques are used and compared as the input feature extraction technique. The system is evaluated using 6 speakers from the King Abdulaziz Arabic Phonetics ...

In hybrid hidden Markov model/artificial neural networks (HMM/ANN) automatic speech recognition (ASR) system, the phoneme class conditional probabilities are estimated by first extracting acoustic features from the speech signal based on prior knowledge such as, speech perception or/and speech production knowledge, and, then modeling the acoustic features with an ANN. Recent advances in machine...

In this paper, a novel architecture is proposed for the speech recognition component in a reading tutor. Decoding starts with an unconstrained phoneme recogniser that produces a phoneme lattice. Next, the best path in the lattice is looked for based on a phoneme level finite state transducer that models the words in the sentence to be read and that includes solutions for expected reading miscue...

In spoken document retrieval, speech recognition is applied to a collection to obtain either words or subword units, such as phonemes, that can be matched against queries. We have explored retrieval based on phoneme n-grams. The use of phonemes addresses the out-of-vocabulary problem, while use of n-grams allows approximate matching on inaccurate phoneme transcriptions. Our experiments explored...

For many years, speech has been the most natural and efficient means of information exchange for human beings. With the advancement of technology and the prevalence of computer usage, the design and production of speech recognition systems have been considered by researchers. Among this, lip-reading techniques encountered with many challenges for speech recognition, that one of the challenges b...

We describe an extension to the Baum-Welch algorithm for training Hidden Markov Models that uses explicit phoneme segmentation to constrain the forward and backward lattice. The HMMs trained with this algorithm can be shown to improve the accuracy of automatic phoneme segmentation. In addition, this algorithm is significantly more computationally efficient than the full BaumWelch algorithm, whi...

The goal of this work is to present a text-to-phoneme conversion (TPC) check tool for generating dictionaries usable for Speech Synthesis and Speech Recognition which includes phonetic and linguistic knowledge. The aim is to improve and accelerate the task of producing canonic pronunciation dictionaries in languages where no simple text-to-phoneme conversion is possible. The prototype was devel...