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SUMMARY:Recognising South African Voices: A Multilingual ASR Pipeline
DTSTART;VALUE=DATE-TIME:20251203T142000Z
DTEND;VALUE=DATE-TIME:20251203T143500Z
DTSTAMP;VALUE=DATE-TIME:20260711T074722Z
UID:indico-contribution-3048@events.chpc.ac.za
DESCRIPTION:Speakers: Mahlatse Mbooi (CSIR)\nSouth Africa’s rich linguis
 tic diversity poses unique challenges for artificial intelligence systems\
 , particularly in automatic speech recognition (ASR) where multilingual sp
 eakers frequently switch languages mid-conversation. This study proposes a
  robust ASR pipeline tailored for code-switched speech in health settings\
 , addressing practical issues such as overlapping dialogue\, background no
 ise\, and inconsistent language usage. The pipeline will integrate multili
 ngual acoustic models and language-specific preprocessing techniques\, tra
 ined on a standardised dataset comprising South African languages includin
 g isiZulu\, Sepedi and English.\nBy focusing on pipeline design\, dataset 
 standardisation and multilingual integration\, this work demonstrates how 
 AI can be built to truly understand South African voices rather than ignor
 ing them. Structured and reproducible approaches to code-switched data lay
  the foundation for inclusive\, fair\, and context-aware AI that represent
 s local language communities and highlight the broader opportunities for l
 everaging multilingual data responsibly.\n\nhttps://events.chpc.ac.za/even
 t/155/contributions/3048/
LOCATION:Century City Conference Centre 1/1-7 - Room 7
URL:https://events.chpc.ac.za/event/155/contributions/3048/
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