The evaluation of a code-switched Sepedi-English automatic speech
  recognition system
Amanda Phaladi Thipe Modipa
Speech technology is a field that encompasses various techniques and tools
used to enable machines to interact with speech, such as automatic speech
recognition (ASR), spoken dialog systems, and others, allowing a device to
capture spoken words through a microphone from a human speaker. End-to-end
approaches such as Connectionist Temporal Classification (CTC) and
attention-based methods are the most used for the development of ASR systems.
However, these techniques were commonly used for research and development for
many high-resourced languages with large amounts of speech data for training
and evaluation, leaving low-resource languages relatively underdeveloped. While
the CTC method has been successfully used for other languages, its
effectiveness for the Sepedi language remains uncertain. In this study, we
present the evaluation of the Sepedi-English code-switched automatic speech
recognition system. This end-to-end system was developed using the Sepedi
Prompted Code Switching corpus and the CTC approach. The performance of the
system was evaluated using both the NCHLT Sepedi test corpus and the Sepedi
Prompted Code Switching corpus. The model produced the lowest WER of 41.9%,
however, the model faced challenges in recognizing the Sepedi only text.