# Speech Analysis of Language Varieties in Italy

arXiv: https://arxiv.org/abs/2406.15862 · HTML: https://mlsanity.com/paper/2406.15862/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 63 references that are in the map.
- First posted on arXiv 22 Jun 2024.

## Builds on (most cited first, 27 of 27)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (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)
- [Unsupervised Cross-lingual Representation Learning for Speech Recognition](https://mlsanity.com/paper/2006.13979/) (2020)
- [MLS: A Large-Scale Multilingual Dataset for Speech Research](https://mlsanity.com/paper/2012.03411/) (2020)
- [Libri-Light: A Benchmark for ASR with Limited or No Supervision](https://mlsanity.com/paper/1912.07875/) (2019)
- [VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation](https://mlsanity.com/paper/2101.00390/) (2021)
- [SpeechBrain: A General-Purpose Speech Toolkit](https://mlsanity.com/paper/2106.04624/) (2021)
- [GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audio](https://mlsanity.com/paper/2106.06909/) (2021)
- [Multi-Similarity Loss with General Pair Weighting for Deep Metric Learning](https://mlsanity.com/paper/1904.06627/) (2019)
- [DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations](https://mlsanity.com/paper/2006.03659/) (2020)
- [VoxLingua107: a Dataset for Spoken Language Recognition](https://mlsanity.com/paper/2011.12998/) (2020)
- [CLAR: Contrastive Learning of Auditory Representations](https://mlsanity.com/paper/2010.09542/) (2020)
- [LLaMAntino: LLaMA 2 Models for Effective Text Generation in Italian Language](https://mlsanity.com/paper/2312.09993/) (2023)
- [Almawave-SLU: A new dataset for SLU in Italian](https://mlsanity.com/paper/1907.07526/) (2019)
- [ITALIC: An Italian Intent Classification Dataset](https://mlsanity.com/paper/2306.08502/) (2023)
- [Continuous Representation of Location for Geolocation and Lexical Dialectology using Mixture Density Networks](https://mlsanity.com/paper/1708.04358/) (2017)
- [Finnish Dialect Identification: The Effect of Audio and Text](https://mlsanity.com/paper/2111.03800/) (2021)
- [Lahjoita puhetta -- a large-scale corpus of spoken Finnish with some benchmarks](https://mlsanity.com/paper/2203.12906/) (2022)
- [Accidental Learners: Spoken Language Identification in Multilingual Self-Supervised Models](https://mlsanity.com/paper/2211.05103/) (2022)
- [RoDia: A New Dataset for Romanian Dialect Identification from Speech](https://mlsanity.com/paper/2309.03378/) (2023)
- [The 2022 NIST Language Recognition Evaluation](https://mlsanity.com/paper/2302.14624/) (2023)
- [North S'ami Dialect Identification with Self-supervised Speech Models](https://mlsanity.com/paper/2305.11864/) (2023)

## Built on by (most cited first, 3 of 3)

- [Dialetto, ma Quanto Dialetto? Transcribing and Evaluating Dialects on a Continuum](https://mlsanity.com/paper/2410.14589/) (2024)
- [MoRoVoc: A Large Dataset for Geographical Variation Identification of the Spoken Romanian Language](https://mlsanity.com/paper/2509.16781/) (2025)
- [A Survey on Spoken Italian Datasets and Corpora](https://mlsanity.com/paper/2501.06557/) (2025)

## Abstract (arXiv)

Italy exhibits rich linguistic diversity across its territory due to the distinct regional languages spoken in different areas. Recent advances in self-supervised learning provide new opportunities to analyze Italy's linguistic varieties using speech data alone. This includes the potential to leverage representations learned from large amounts of data to better examine nuances between closely related linguistic varieties. In this study, we focus on automatically identifying the geographic region of origin of speech samples drawn from Italy's diverse language varieties. We leverage self-supervised learning models to tackle this task and analyze differences and similarities between Italy's regional languages. In doing so, we also seek to uncover new insights into the relationships among these diverse yet closely related varieties, which may help linguists understand their interconnected evolution and regional development over time and space. To improve the discriminative ability of learned representations, we evaluate several supervised contrastive learning objectives, both as pre-training steps and additional fine-tuning objectives. Experimental evidence shows that pre-trained self-supervised models can effectively identify regions from speech recording. Additionally, incorporating contrastive objectives during fine-tuning improves classification accuracy and yields embeddings that distinctly separate regional varieties, demonstrating the value of combining self-supervised pre-training and contrastive learning for this task.