Pronunciation of the phonemic inventory of a new language often presents difficulties to second language (L2) learners. These challenges can be alleviated by the development of pronunciation feedback tools that take speech input from learners and return information about errors in the utterance. This paper presents the development of a corpus designed for use in pronunciation feedback research. The corpus is comprised of gold standard recordings from isiZulu teachers and recordings from isiZulu L2 learners that have been annotated for pronunciation errors. Exploring the potential benefits of word-level versus phoneme-level feedback necessitates a speech corpus that has been annotated for errors on the phoneme-level. To aid in this discussion, this corpus of isiZulu L2 speech has been annotated for phoneme-errors in utterances, as well as suprasegmental errors in tone.

Orthographical standardization is a milestone in a language’s documentation and the development of its resources. However, texts written in former orthographies remain relevant to the language’s history and development and therefore must be converted to the standardized orthography. Ensuring a language has access to the orthographically standardized version of all of its recorded texts is important in the development of resources as it provides additional textual resources for training, supports contribution of authors using former writing systems, and provides information about the development of the language. This paper evaluates the performance of natural language processing methods, specifically Finite State Transducers and Long Short-term Memory networks, for the orthographical conversion of Bàsàá texts from the Protestant missionary orthography to the now-standard AGLC orthography, with the conclusion that LSTMs are somewhat more effective in the absence of explicit lexical information.

Abusive language detection has become an important tool for the cultivation of safe online platforms. We investigate the interaction of annotation quality and classifier performance. We use a new, fine-grained annotation scheme that allows us to distinguish between abusive language and colloquial uses of profanity that are not meant to harm. Our results show a tendency of crowd workers to overuse the abusive class, which creates an unrealistic class balance and affects classification accuracy. We also investigate different methods of distinguishing between explicit and implicit abuse and show lexicon-based approaches either over- or under-estimate the proportion of explicit abuse in data sets.