9 accepted papers
Large-scale pre-training has been shown to benefit speech translation tasks. However, existing multimodal pre-training efforts rely on parallel corpora for semantic alignment, potentially limiting performance to the scale of available data and causing data imbalance. Hence, we propose an adversarial…
End-to-end speech-to-text translation (E2E ST) has increasingly aroused interest and attention recently, attempting to address the problem of data scarcity and modeling burden. Several attempts exploring the combination of Large Speech and Language Models into a unified model to improve E2E ST are c…
Significant progress has recently been made in combining Speech Foundation Models (SFMs) and Large Language Models (LLMs) into a unified model to tackle Speech-to-Text Translation (ST) tasks. However, fine-tuning LLMs to adapt to specific downstream tasks requires substantial resources, which is oft…
Direction-based target speaker extraction (TSE) attracts a constant attention due to the convenience of direction acquisition over assistive video or enrollment audio. The direction clue heavily affects the TSE performance, which might be more seriously in the case of binaural setups due to the smal…
Large language models (LLMs) rely on massive amounts of training data, however, the quantity of empirically observed data is limited. To alleviate this issue, lots of LLMs leverage synthetic data to enhance the quantity of training data. Despite significant advancements in LLMs, the efficiency and s
Existing state-of-the-art techniques that employ multilingual sentence embeddings for mining parallel texts predominantly rely on extensive supervision, which often results in sub-optimal performance in the absence of large-scale parallel training datasets. In this study, we introduce a novel method…
Recent studies have attempted to refine the Transformer architecture to demonstrate its effectiveness in Long-Term Time Series Forecasting (LTSF) tasks. Despite surpassing many linear forecasting models with ever-improving performance, we remain skeptical of Transformers as a solution for LTSF. We a…
Target speaker extraction (TSE) based on direction of arrival (DOA) has a wide range of applications in e.g., remote conferencing, hearing aids, in-car speech interaction. Due to the inherent phase uncertainty, existing TSE methods usually suffer from speaker confusion within specific frequency band…
End-to-end paradigm has aroused more and more interests and attention for improving speech-to-text translation (ST) recently. Existing end-to-end models mainly attributes and attempts to address the problem of modeling burden and data scarcity, while always fail to maintain both cross-modal and cros…