This research focuses on understanding the information encoded in speech processing by using vector representations of speech from a pretrained model. The study proposes an unsupervised method using ABX tests…
Continue readingThe article discusses the effectiveness of Parameter-Efficient Fine-Tuning (PEFT) in speech processing, with a particular focus on the optimal placement of PEFT methods, their merging strategies, and the use of…
Continue readingThis study focuses on Parameter-Efficient Fine-Tuning (PEFT) in speech processing, exploring optimal placement and merging strategies, as well as the use of ensemble learning. The results reveal that Differentiable Architecture…
Continue readingThe article discusses the use of Parameter-Efficient Fine-Tuning (PEFT) in speech processing. The authors conducted experiments to compare different PEFT methods and their layer-wise placement using Differentiable Architecture Search (DARTS)….
Continue readingThe article presents the Deep Audio Analyzer, an open-source framework aiming to simplify the research and development process of neural speech processing pipelines. The tool is designed to support several…
Continue readingThe 2023 Multilingual Speech Universal Performance Benchmark (ML-SUPERB) Challenge expands on the acclaimed SUPERB framework. It emphasizes self-supervised models in multilingual speech recognition and language identification. The challenge consists of…
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