# A Compact End-to-End Model with Local and Global Context for Spoken Language Identification

arXiv: https://arxiv.org/abs/2210.15781 · HTML: https://mlsanity.com/paper/2210.15781/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 34 references that are in the map.
- First posted on arXiv 27 Oct 2022.

## Builds on (most cited first, 20 of 20)

- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [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)
- [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://mlsanity.com/paper/2111.09296/) (2021)
- [FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech](https://mlsanity.com/paper/2205.12446/) (2022)
- [SpeechBrain: A General-Purpose Speech Toolkit](https://mlsanity.com/paper/2106.04624/) (2021)
- [NeMo: a toolkit for building AI applications using Neural Modules](https://mlsanity.com/paper/1909.09577/) (2019)
- [VoxLingua107: a Dataset for Spoken Language Recognition](https://mlsanity.com/paper/2011.12998/) (2020)
- [ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context](https://mlsanity.com/paper/2005.03191/) (2020)
- [QuartzNet: Deep Automatic Speech Recognition with 1D Time-Channel Separable Convolutions](https://mlsanity.com/paper/1910.10261/) (2019)
- [TitaNet: Neural Model for speaker representation with 1D Depth-wise separable convolutions and global context](https://mlsanity.com/paper/2110.04410/) (2021)
- [Exploring the Encoding Layer and Loss Function in End-to-End Speaker and Language Recognition System](https://mlsanity.com/paper/1804.05160/) (2018)
- [Citrinet: Closing the Gap between Non-Autoregressive and Autoregressive End-to-End Models for Automatic Speech Recognition](https://mlsanity.com/paper/2104.01721/) (2021)
- [XTREME-S: Evaluating Cross-lingual Speech Representations](https://mlsanity.com/paper/2203.10752/) (2022)
- [Improving Language Identification of Accented Speech](https://mlsanity.com/paper/2203.16972/) (2022)
- [ConvRNN-T: Convolutional Augmented Recurrent Neural Network Transducers for Streaming Speech Recognition](https://mlsanity.com/paper/2209.14868/) (2022)
- [PHO-LID: A Unified Model Incorporating Acoustic-Phonetic and Phonotactic Information for Language Identification](https://mlsanity.com/paper/2203.12366/) (2022)
- [Utterance-level end-to-end language identification using attention-based CNN-BLSTM](https://mlsanity.com/paper/1902.07374/) (2019)
- [Transducer-based language embedding for spoken language identification](https://mlsanity.com/paper/2204.03888/) (2022)

## Built on by (most cited first, 5 of 5)

- [Exploring Spoken Language Identification Strategies for Automatic Transcription of Multilingual Broadcast and Institutional Speech](https://mlsanity.com/paper/2406.09290/) (2024)
- [Exploring the Impact of Data Quantity on ASR in Extremely Low-resource Languages](https://mlsanity.com/paper/2409.08872/) (2024)
- [Geolocation-Aware Robust Spoken Language Identification](https://mlsanity.com/paper/2508.17148/) (2025)
- [Acoustic domain shift in spoken language identification from systematic domain generalization evaluation to real-world application](https://mlsanity.com/paper/2609.31759/) (2026)
- [Anuvani-LID: Robust and Calibrated Spoken Language Identification for Indian Languages](https://mlsanity.com/paper/2610.20263/) (2026)

## Abstract (arXiv)

We introduce TitaNet-LID, a compact end-to-end neural network for Spoken Language Identification (LID) that is based on the ContextNet architecture. TitaNet-LID employs 1D depth-wise separable convolutions and Squeeze-and-Excitation layers to effectively capture local and global context within an utterance. Despite its small size, TitaNet-LID achieves performance similar to state-of-the-art models on the VoxLingua107 dataset while being 10 times smaller. Furthermore, it can be easily adapted to new acoustic conditions and unseen languages through simple fine-tuning, achieving a state-of-the-art accuracy of 88.2% on the FLEURS benchmark. Our model is scalable and can achieve a better trade-off between accuracy and speed. TitaNet-LID performs well even on short utterances less than 5s in length, indicating its robustness to input length.