Cross-dialectal acoustic data sharing for Arabic speech recognition
The automatic recognition of Arabic dialectal speech is a challenging task since Arabic dialects are essentially spoken varieties, for which only sparse resources (transcriptions and standardized acoustic data) are available to date. In this paper we describe the use of acoustic data from modern standard Arabic (MSA) to improve the recognition of Egyptian conversational Arabic (ECA). The cross-dialectal use of data is complicated by the fact that MSA is written without short vowels and other diacritics and thus has incomplete phonetic information. This problem is addressed by automatically vowelizing MSA data before combining it with ECA data. We described the vowelization procedure as well as speech recognition experiments and show that our technique yields improvements over our baseline system.