  This paper presents a novel Dialectal Sound and Vowelization Recovery
framework, designed to recognize borrowed and dialectal sounds within
phonologically diverse and dialect-rich languages, that extends beyond its
standard orthographic sound sets. The proposed framework utilized a quantized
sequence of input with(out) continuous pretrained self-supervised
representation. We show the efficacy of the pipeline using limited data for
Arabic, a dialect-rich language containing more than 22 major dialects.
Phonetically correct transcribed speech resources for dialectal Arabic are
scarce. Therefore, we introduce ArabVoice15, a first-of-its-kind, curated test
set featuring 5 hours of dialectal speech across 15 Arab countries, with
phonetically accurate transcriptions, including borrowed and dialect-specific
sounds. We described in detail the annotation guideline along with the analysis
of the dialectal confusion pairs. Our extensive evaluation includes both
subjective -- human perception tests and objective measures. Our empirical
results, reported with three test sets, show that with only one and half hours
of training data, our model improve character error rate by ~ 7\% in
ArabVoice15 compared to the baseline.
