# That Sounds Familiar: an Analysis of Phonetic Representations Transfer Across Languages

arXiv: https://arxiv.org/abs/2005.08118 · HTML: https://mlsanity.com/paper/2005.08118/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2020.

## Built on by (most cited first, 10 of 10)

- [Whistle: Data-Efficient Multilingual and Crosslingual Speech Recognition via Weakly Phonetic Supervision](https://mlsanity.com/paper/2406.02166/) (2024)
- [Multilingual and crosslingual speech recognition using phonological-vector based phone embeddings](https://mlsanity.com/paper/2107.05038/) (2021)
- [SQuId: Measuring Speech Naturalness in Many Languages](https://mlsanity.com/paper/2210.06324/) (2022)
- [Discovering Phonetic Inventories with Crosslingual Automatic Speech Recognition](https://mlsanity.com/paper/2201.11207/) (2022)
- [Investigating the Impact of Cross-lingual Acoustic-Phonetic Similarities on Multilingual Speech Recognition](https://mlsanity.com/paper/2207.03390/) (2022)
- [An Empirical Recipe for Universal Phone Recognition](https://mlsanity.com/paper/2603.29042/) (2026)
- [Language-Universal Phonetic Representation in Multilingual Speech Pretraining for Low-Resource Speech Recognition](https://mlsanity.com/paper/2305.11569/) (2023)
- [Unsupervised Acoustic Unit Discovery by Leveraging a Language-Independent Subword Discriminative Feature Representation](https://mlsanity.com/paper/2104.00994/) (2021)
- [Non-Linear Pairwise Language Mappings for Low-Resource Multilingual Acoustic Model Fusion](https://mlsanity.com/paper/2207.03391/) (2022)
- [Language-universal phonetic encoder for low-resource speech recognition](https://mlsanity.com/paper/2305.11576/) (2023)

## Abstract (arXiv)

Only a handful of the world's languages are abundant with the resources that enable practical applications of speech processing technologies. One of the methods to overcome this problem is to use the resources existing in other languages to train a multilingual automatic speech recognition (ASR) model, which, intuitively, should learn some universal phonetic representations. In this work, we focus on gaining a deeper understanding of how general these representations might be, and how individual phones are getting improved in a multilingual setting. To that end, we select a phonetically diverse set of languages, and perform a series of monolingual, multilingual and crosslingual (zero-shot) experiments. The ASR is trained to recognize the International Phonetic Alphabet (IPA) token sequences. We observe significant improvements across all languages in the multilingual setting, and stark degradation in the crosslingual setting, where the model, among other errors, considers Javanese as a tone language. Notably, as little as 10 hours of the target language training data tremendously reduces ASR error rates. Our analysis uncovered that even the phones that are unique to a single language can benefit greatly from adding training data from other languages - an encouraging result for the low-resource speech community.