Investigating model performance in language identification: beyond simple error statistics
This addresses algorithmic bias in language identification for language development experts, though it is incremental as it focuses on evaluation methods rather than new models.
The paper investigates language identification systems' performance on individual recordings and speech units with different linguistic properties, revealing systematic errors masked by overview metrics like equal error rate, using the MERLIon CCS Challenge dataset of accented English-Mandarin code-switched child-directed speech.
Language development experts need tools that can automatically identify languages from fluent, conversational speech, and provide reliable estimates of usage rates at the level of an individual recording. However, language identification systems are typically evaluated on metrics such as equal error rate and balanced accuracy, applied at the level of an entire speech corpus. These overview metrics do not provide information about model performance at the level of individual speakers, recordings, or units of speech with different linguistic characteristics. Overview statistics may therefore mask systematic errors in model performance for some subsets of the data, and consequently, have worse performance on data derived from some subsets of human speakers, creating a kind of algorithmic bias. In the current paper, we investigate how well a number of language identification systems perform on individual recordings and speech units with different linguistic properties in the MERLIon CCS Challenge. The Challenge dataset features accented English-Mandarin code-switched child-directed speech.