Robust Open-Set Spoken Language Identification and the CU MultiLang Dataset

• URL: http://arxiv.org/abs/2308.14951v1
• Date: Tue, 29 Aug 2023 00:44:27 GMT
• Title: Robust Open-Set Spoken Language Identification and the CU MultiLang Dataset
• Authors: Mustafa Eyceoz, Justin Lee, Siddharth Pittie, Homayoon Beigi
• Abstract summary: Open-set spoken language identification systems can detect when an input exhibits none of the original languages. We implement a novel approach to open-set spoken language identification that uses MFCC and pitch features. 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.
• Score: 2.048226951354646
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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