# An Overview of Indian Spoken Language Recognition from Machine Learning Perspective arXiv: https://arxiv.org/abs/2212.03812 ยท HTML: https://mlsanity.com/paper/2212.03812/ ## Key facts - Cited by 2 papers in the map (data through 14 Oct 2026). - Builds on 28 of its 215 references that are in the map. - First posted on arXiv 30 Nov 2022. ## Builds on (most cited first, 28 of 28) - [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019) - [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017) - [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019) - [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019) - [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020) - [vq-wav2vec: Self-Supervised Learning of Discrete Speech Representations](https://mlsanity.com/paper/1910.05453/) (2019) - [A Comparative Study on Transformer vs RNN in Speech Applications](https://mlsanity.com/paper/1909.06317/) (2019) - [Multi-task self-supervised learning for Robust Speech Recognition](https://mlsanity.com/paper/2001.09239/) (2020) - [VoxLingua107: a Dataset for Spoken Language Recognition](https://mlsanity.com/paper/2011.12998/) (2020) - [Domain Adversarial Training for Accented Speech Recognition](https://mlsanity.com/paper/1806.02786/) (2018) - [Trainable Frontend For Robust and Far-Field Keyword Spotting](https://mlsanity.com/paper/1607.05666/) (2016) - [x-vectors meet emotions: A study on dependencies between emotion and speaker recognition](https://mlsanity.com/paper/2002.05039/) (2020) - [Improving Cross-Lingual Transfer Learning for End-to-End Speech Recognition with Speech Translation](https://mlsanity.com/paper/2006.05474/) (2020) - [Self-supervised speaker embeddings](https://mlsanity.com/paper/1904.03486/) (2019) - [On Cross-Corpus Generalization of Deep Learning Based Speech Enhancement](https://mlsanity.com/paper/2002.04027/) (2020) - [Transfer Learning for Improving Speech Emotion Classification Accuracy](https://mlsanity.com/paper/1801.06353/) (2018) - [AP20-OLR Challenge: Three Tasks and Their Baselines](https://mlsanity.com/paper/2006.03473/) (2020) - [Generalization of Spoofing Countermeasures: a Case Study with ASVspoof 2015 and BTAS 2016 Corpora](https://mlsanity.com/paper/1901.08025/) (2019) - [AP19-OLR Challenge: Three Tasks and Their Baselines](https://mlsanity.com/paper/1907.07626/) (2019) - [AP16-OL7: A Multilingual Database for Oriental Languages and A Language Recognition Baseline](https://mlsanity.com/paper/1609.08445/) (2016) - [Exploiting Spectral Augmentation for Code-Switched Spoken Language Identification](https://mlsanity.com/paper/2010.07130/) (2020) - [Dual Script E2E framework for Multilingual and Code-Switching ASR](https://mlsanity.com/paper/2106.01400/) (2021) - [AP17-OLR Challenge: Data, Plan, and Baseline](https://mlsanity.com/paper/1706.09742/) (2017) - [Cross-Corpora Language Recognition: A Preliminary Investigation with Indian Languages](https://mlsanity.com/paper/2105.04639/) (2021) - [MicAugment: One-shot Microphone Style Transfer](https://mlsanity.com/paper/2010.09658/) (2020) - [Multilingual Bottleneck Features for Improving ASR Performance of Code-Switched Speech in Under-Resourced Languages](https://mlsanity.com/paper/2011.03118/) (2020) - [Channel adversarial training for cross-channel text-independent speaker recognition](https://mlsanity.com/paper/1902.09074/) (2019) - [Dynamic Multi-scale Convolution for Dialect Identification](https://mlsanity.com/paper/2108.07787/) (2021) ## Built on by (most cited first, 2 of 2) - [Cross-Corpora Spoken Language Identification with Domain Diversification and Generalization](https://mlsanity.com/paper/2302.05110/) (2023) - [Wavelet Scattering Transform for Improving Generalization in Low-Resourced Spoken Language Identification](https://mlsanity.com/paper/2310.00602/) (2023) ## Abstract (arXiv) Automatic spoken language identification (LID) is a very important research field in the era of multilingual voice-command-based human-computer interaction (HCI). A front-end LID module helps to improve the performance of many speech-based applications in the multilingual scenario. India is a populous country with diverse cultures and languages. The majority of the Indian population needs to use their respective native languages for verbal interaction with machines. Therefore, the development of efficient Indian spoken language recognition systems is useful for adapting smart technologies in every section of Indian society. The field of Indian LID has started gaining momentum in the last two decades, mainly due to the development of several standard multilingual speech corpora for the Indian languages. Even though significant research progress has already been made in this field, to the best of our knowledge, there are not many attempts to analytically review them collectively. In this work, we have conducted one of the very first attempts to present a comprehensive review of the Indian spoken language recognition research field. In-depth analysis has been presented to emphasize the unique challenges of low-resource and mutual influences for developing LID systems in the Indian contexts. Several essential aspects of the Indian LID research, such as the detailed description of the available speech corpora, the major research contributions, including the earlier attempts based on statistical modeling to the recent approaches based on different neural network architectures, and the future research trends are discussed. This review work will help assess the state of the present Indian LID research by any active researcher or any research enthusiasts from related fields.