Marco Dinarelli

Effective Spoken Language Labeling with Deep Recurrent Neural Networks

2017 · arXiv (Cornell University)

Understanding spoken language is a highly complex problem, which can be decomposed into several simpler tasks. In this paper, we focus on Spoken Language Understanding (SLU), the module of spoken dialog systems responsible for extracting …

An Analysis of Linear Complexity Attention Substitutes with BEST-RQ

2024 · arXiv (Cornell University)

Self-Supervised Learning (SSL) has proven to be effective in various domains, including speech processing. However, SSL is computationally and memory expensive. This is in part due the quadratic complexity of multi-head self-attention (MHSA). Alternatives for …