# SAMU-XLSR: Semantically-Aligned Multimodal Utterance-level Cross-Lingual Speech Representation

**Authors:** Sameer Khurana, Antoine Laurent, James Glass  
**arXiv:** [2205.08180](https://arxiv.org/abs/2205.08180)  
**Date:** 2022-05-17  
**Summary page:** https://www.summarizepaper.com/en/paper/2205.08180v1/

## Key points

- SAMU-XLSR is a Semantically-Aligned Multimodal Utterance-level Cross-Lingual Speech Representation learning framework
- It combines XLS-R and LaBSE models for multilingual acoustic frame-level speech representation learning and text sentence encoding, respectively
- SAMU-XLSR creates an embedding vector space that is semantically aligned across different languages
- Despite using only multilingual transcribed speech data for training, cross-lingual speech-text and speech-speech associations emerge in the learned representation space
- SAMU-XLSR is validated through cross-lingual speech to text and speech to speech translation retrieval tasks using a pre-trained LaBSE text sentence encoder
- The framework performs well in various cross lingual translation retrieval tasks and has potential applications in domains requiring cross lingual understanding and translation capabilities.

## Summary

The SAMU-XLSR framework is proposed as a Semantically-Aligned Multimodal Utterance-level Cross-Lingual Speech Representation learning framework. It combines two state-of-the-art models: XLS-R, which is a multilingual acoustic frame-level speech representation learning model, and LaBSE (Language Agnostic BERT Sentence Embedding), which is a text sentence encoder. This allows for the creation of an embedding vector space that is semantically aligned across different languages. The training data for SAMU-XLSR consists of only multilingual transcribed speech data. Despite this limitation, cross-lingual speech-text and speech-speech associations emerge in the learned representation space of SAMU-XLSR. To validate their claims, the authors use the SAMU-XLSR speech encoder in combination with a pre-trained LaBSE text sentence encoder for cross-lingual speech to text translation retrieval. They also use SAMU XLSR alone for cross lingual speech to speech translation retrieval. The performance of these applications is evaluated through several cross lingual text and speech translation retrieval tasks across multiple datasets. In summary, the proposed SAMU XLSR framework focuses on learning multimodal multilingual speech embedding at the sentence level to create a semantically aligned embedding vector space across different languages. The framework shows promising results in cross lingual translation retrieval tasks and offers potential applications in various domains requiring cross lingual understanding and translation capabilities.