Middle eastern and north african english speech corpus (Menaesc): Automatic identification of mena english accents
الملخص
This study aims to explore the English accents in the Arab world. Although there are limited resources for a speech
corpus that attempts to automatically identify the degree of accent patterns of an Arabic speaker of English, there is no speech
corpus specialized for Arabic speakers of English in the Middle East and North Africa (MENA). To that end, different samples
were collected in order to create the linguistic resource that we called Middle Eastern and North African English Speech
Corpus (MENAESC). In addition to the “accent approach” applied in the field of automatic language/dialect recognition; we
applied also the “macro-accent approach” -by employing Mel-Frequency Cepstral Coefficients (MFCC), Energy and Shifted
Delta Cepstra (SDC) features and Gaussian Mixture Model-Universal Background Model (GMM-UBM) classifier- on four
accents (Egyptian, Qatari, Syrian, and Tunisian accents) among the eleven accents that were selected based on their high
population density in the location where the experiments were carried out. By using the Equal Error Rate percentage (EER%)
for the assessment of our system effectiveness in the identification of MENA English accents using the two approaches
mentioned above through the employ of the MENAESC, results showed we reached 1.5 to 2%, for “accent approach” and 2 to
3.5% for “macro-accents approach” for identification of MENA English. It also exhibited that the Qatari accent, of the 4
accents included, scored the lowest EER% for all tests performed. Taken together, the system effectiveness is not only affected
by the approaches used, but also by the database size MENAESC and its characteristics. Moreover, it is impacted by the
proficiency of the Arabic speakers of English and the influence of their mother tongue.
المجموعات
- علوم وهندسة الحاسب [2288 items ]