Reading materials

• Li, H. et al. (2013). Spoken Language Recognition: From Fundamentals to Practice. Thorough overview of language recognition.
• Tang, Z. et al. (2018). Phonetic Temporal Neural Model for Language Identification. Sections I.A and I.B provide another short overview.
• Gonzalez-Dominguez, Javier et al. (2014). Automatic language identification using long short-term memory recurrent neural networks. Deep learning approach with DNNs and LSTMs.
• Martínez, David et al. (2011). Language Recognition in iVectors Space. Statistical approach.
• Zissman, M. A. (1996). Comparison of four approaches to automatic language identification of telephone speech. Comparing GMMs and PRLM variants.
• Muthusamy, Y. K. (1994). Reviewing automatic  language identication.
• Castaldo, F. et al. (2008). Politecnico di Torino System for the 2007 NIST Language Recognition Evaluation.

Examples of state-of-the-art models:

• Shon, Suwon et al. (2018). Convolutional Neural Network and Language Embeddings for End-to-End Dialect Recognition. Model: https://github.com/swshon/dialectID_e2e.
• Ma, Zhanyu et al. (2019). Short Utterance Based Speech Language Identification in Intelligent Vehicles With Time-Scale Modifications and Deep Bottleneck Features.