ELEC-E5510 - Speech Recognition D, Lecture, 26.10.2022-9.12.2022
This course space end date is set to 09.12.2022 Search Courses: ELEC-E5510
Reading materials
Completion requirements
Collection of reading materials by topic.
2. Material on project topics
2.3. Language recognition
- 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.