# Modernizing Open-Set Speech Language Identification

arXiv: https://arxiv.org/abs/2205.10397 · HTML: https://mlsanity.com/paper/2205.10397/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 16 references that are in the map.
- First posted on arXiv 20 May 2022.

## Builds on (most cited first, 7 of 7)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [VoxCeleb2: Deep Speaker Recognition](https://mlsanity.com/paper/1806.05622/) (2018)
- [MediaSpeech: Multilanguage ASR Benchmark and Dataset](https://mlsanity.com/paper/2103.16193/) (2021)
- [Language Identification Using Deep Convolutional Recurrent Neural Networks](https://mlsanity.com/paper/1708.04811/) (2017)
- [A reproduction of Apple's bi-directional LSTM models for language identification in short strings](https://mlsanity.com/paper/2102.06282/) (2021)
- [Pansori: ASR Corpus Generation from Open Online Video Contents](https://mlsanity.com/paper/1812.09798/) (2018)
- [Is Attention always needed? A Case Study on Language Identification from Speech](https://mlsanity.com/paper/2110.03427/) (2021)

## Built on by (most cited first, 1 of 1)

- [Robust Open-Set Spoken Language Identification and the CU MultiLang Dataset](https://mlsanity.com/paper/2308.14951/) (2023)

## Abstract (arXiv)

While most modern speech Language Identification methods are closed-set, we want to see if they can be modified and adapted for the open-set problem. When switching to the open-set problem, the solution gains the ability to reject an audio input when it fails to match any of our known language options. We tackle the open-set task by adapting two modern-day state-of-the-art approaches to closed-set language identification: the first using a CRNN with attention and the second using a TDNN. In addition to enhancing our input feature embeddings using MFCCs, log spectral features, and pitch, we will be attempting two approaches to out-of-set language detection: one using thresholds, and the other essentially performing a verification task. We will compare both the performance of the TDNN and the CRNN, as well as our detection approaches.