Robust Open-Set Spoken Language Identification and the CU MultiLang Dataset
This addresses the limitation of closed-set models for real-world language identification applications, though it is incremental in combining existing techniques.
The paper tackles the problem of open-set spoken language identification, enabling detection of languages not seen during training, and achieves 91.76% accuracy on trained languages while adapting to unknown ones.
Most state-of-the-art spoken language identification models are closed-set; in other words, they can only output a language label from the set of classes they were trained on. Open-set spoken language identification systems, however, gain the ability to detect when an input exhibits none of the original languages. In this paper, we implement a novel approach to open-set spoken language identification that uses MFCC and pitch features, a TDNN model to extract meaningful feature embeddings, confidence thresholding on softmax outputs, and LDA and pLDA for learning to classify new unknown languages. We present a spoken language identification system that achieves 91.76% accuracy on trained languages and has the capability to adapt to unknown languages on the fly. To that end, we also built the CU MultiLang Dataset, a large and diverse multilingual speech corpus which was used to train and evaluate our system.