Categories: FAANG

Towards Real-World Streaming Speech Translation for Code-Switched Speech

This paper was accepted at the EMNLP Workshop on Computational Approaches to Linguistic Code-Switching (CALCS).
Code-switching (CS), i.e. mixing different languages in a single sentence, is a common phenomenon in communication and can be challenging in many Natural Language Processing (NLP) settings. Previous studies on CS speech have shown promising results for end-to-end speech translation (ST), but have been limited to offline scenarios and to translation to one of the languages present in the source (monolingual transcription).
In this paper, we focus on two essential yet unexplored areas…
AI Generated Robotic Content

Recent Posts

REFACTOR-VLA: Unsupervised Library Learning of Typed Motor Programs

Most current vision-language-action (VLA) models—such as OpenVLA, π0, RT-2, and RDT-1B—are “monolithic.” This means they…

31 mins ago

Accessing OpenAI models on Amazon Bedrock from Australia with global cross-Region inference

Australian teams working with OpenAI models can now access the latest OpenAI models through Amazon…

31 mins ago

Getting started with Mantis, our open-source bug finding-and-fixing harness

AI models have clearly proven their ability to discover and exploit vulnerabilities without much, if…

31 mins ago

Meta Pushes Its New AI Agent on Employees—but Eases Off on Tokenmaxxing

The company is reducing pressure on workers to use artificial intelligence tools while encouraging them…

2 hours ago

Why did your robotaxi stop? New system helps predict self-driving car mistakes

Self-driving cars are often controlled by deep learning models that sometimes fail in unexpected situations.…

2 hours ago

Introducing Claude Fable 5.1 on AWS

Today, we’re excited to announce the availability of Claude Fable 5.1 on Amazon Bedrock and…

1 day ago