Search

This paper presents a technique that applies the pairwise variability index (PVI), a rhythm metric that quantifies variability in speech rhythm, to the classification of speech varieties. The technique combines the Particle Swarm Optimization (PSO) algorithm with a generalization of several rhythm metrics that are based on the PVI. The performance of this optimization-oriented classification is compared with classification that uses conventional (both PVI-based and interval-based) rhythm metrics. Application is made to the classification of native and non-native Arabic speech using data are from the West Point Arabic Speech Corpus; experiments are based on segmental durations and use Support Vector Machine (SVM) classification. Results show that the optimization-oriented classification provides a better discrimination between native and non-native speech varieties than classification based of the conventional rhythm metrics. When added to different combinations of these conventional metrics, the optimization-oriented procedure consistently improves classification rates. [ABSTRACT FROM AUTHOR]

This paper presents the Algerian Arabic Speech Database (ALGASD), a Modern Standard Arabic (MSA) speech corpus composed of utterances pronounced by 300 Algerian native speakers selected from eleven regions of Algeria. One of the objectives of this corpus design is to be representative of the regional accents of MSA spoken in Algeria. Useful information related to the speakers, such as gender, age, and education level, is provided. This paper also reports the results of the Automatic Speech Recognition (ASR) application of the corpus and outlines an original global monophone recognition model designed to handle linguistic variability. The global phone recognition rate for this ASR reference system is satisfactory and may constitute a useful baseline ASR system dedicated to MSA. [ABSTRACT FROM AUTHOR]

Compared to other major languages of the world, the Arabic language suffers from a dearth of research initiatives and research resources. As a result, Modern Standard Arabic (MSA) lacks reliable speech corpora for research in phonetics and related areas of linguistics. In recent years the Linguistic Data Consortium (LDC) published the first public MSA speech corpus designed for speech recognition experiments. That corpus was called West Point. Currently, we are using this corpus in our research experiments for speech recognition and other speech processing investigations. The aim of this paper is to evaluate the West Point Corpus from the MSA phonetic and linguistic point of view. The phonemes used and their numbers, the phoneme definitions, the labeling, and the scripts established by the West Point Corpus are included in the evaluation. Weaknesses, strengths, and discrepancies of the West Point Corpus regarding the linguistic rules and phonetic characteristics of MSA are also discussed in this paper. [ABSTRACT FROM AUTHOR]