The semi-automated creation of stratified speech corpora

Abstract—Smartphones provide an efficient means for the collection of speech data; however, the quality of the corpora created in this fashion is not predictable. We describe an approach that allows us to post-process and rank utterances in a prompted speech corpus quickly and effectively. Utterance ranking makes it possible to both select those utterances with the highest likelihood of being correct and to evaluate the quality of the resulting corpus from a limited sample. This approach has been applied to a collection in the eleven official languages of South Africa, and we show that it naturally leads to the creation of stratified corpora from the same collection. Such corpora can be useful for different purposes, and corpus users are provided with the tools to extract these easily: from small, highly accurate corpora to larger corpora that are likely to contain more errors. Index Terms: speech corpora, automatic speech recognition, confidence scorin