In this work, we explore the benefits of using multilingual bottleneck features (mBNF) in acoustic modelling for the automatic speech recognition of code-switched (CS) speech in African languages.
Automatic segmentation was applied in combination with automaticspeaker diarization.
We present an analysis of semi-supervised acoustic and language model training for English-isiZulu code-switched (CS) ASR using soap opera speech.
This paper reports on the semi-supervised development of acoustic and language models for under-resourced, code-switched speech in five South African languages.
The automatic transcriptions from the best performing pass were used for language model augmentation.
Furthermore, because English is common to all language pairs in our data, it dominates when training a unified language model, leading to improved English ASR performance at the expense of the other languages.
We present our first efforts towards building a single multilingual automatic speech recognition (ASR) system that can process code-switching (CS) speech in five languages spoken within the same population.
We present our first efforts in building an automatic speech recognition system for Somali, an under-resourced language, using 1. 57 hrs of annotated speech for acoustic model training.