  Building automatic speech recognition (ASR) systems is a challenging task,
especially for under-resourced languages that need to construct corpora nearly
from scratch and lack sufficient training data. It has emerged that several
African indigenous languages, including Kiswahili, are technologically
under-resourced. ASR systems are crucial, particularly for the hearing-impaired
persons who can benefit from having transcripts in their native languages.
However, the absence of transcribed speech datasets has complicated efforts to
develop ASR models for these indigenous languages. This paper explores the
transcription process and the development of a Kiswahili speech corpus, which
includes both read-out texts and spontaneous speech data from native Kiswahili
speakers. The study also discusses the vowels and consonants in Kiswahili and
provides an updated Kiswahili phoneme dictionary for the ASR model that was
created using the CMU Sphinx speech recognition toolbox, an open-source speech
recognition toolkit. The ASR model was trained using an extended phonetic set
that yielded a WER and SER of 18.87% and 49.5%, respectively, an improved
performance than previous similar research for under-resourced languages.
