How to learn a better speech representation for end-to-end speech-to-text translation (ST)
with limited labeled data? Existing techniques often attempt to transfer powerful machine …

End-to-end speech translation relies on data that pair source-language speech inputs with
corresponding translations into a target language. Such data are notoriously scarce, making …

We introduces LLaST, a framework for building high-performance Large Language model
based Speech-to-text Translation systems. We address the limitations of end-to-end speech …

End-to-End Speech Translation (E2E-ST) has received increasing attention due to the
potential of its less error propagation, lower latency, and fewer parameters. However, the …

End-to-end speech translation (ST) has attracted substantial attention due to its less error
accumulation and lower latency. Based on triplet ST data⟨ speech-transcription …

In speech translation, leveraging multimodal data to improve model performance and
address limitations of individual modalities has shown significant effectiveness. In this paper …

The black-box nature of end-to-end speech-to-text translation (E2E ST) makes it difficult to
understand how source language inputs are being mapped to the target language. To solve …

It is notoriously difficult to implement end-to-end speech translation (E2E-ST) model
because of the task complexity and data scarcity. Existing techniques often attempt to carry …

Speech recognition specifically, and language technology more generally, have started to
find everyday use. Challenging language tasks have become feasible through a continued …