1 citations · 1 across the 4 of their papers we have counts for
4 papers
Fully Differentiable Neural Forced Alignment via Soft Dynamic Programming
Rotem Rousso, Eyal Cohen, Joseph Keshet
Recent advances in sequence modeling have significantly improved ASR systems, bringing them close to human-level recognition accuracy and enhancing robustness across diverse acoust…
Multilingual Word-Level Forced Alignment with Self-Supervised Representations and Learned Dynamic Programming
Roy Weber, Meidan Zehavi, Rotem Rousso +1
We present a method for accurate multilingual word-level forced alignment, consisting of an alignment encoder and a learned alignment decoder. The encoder integrates two representa…
How Does a Deep Neural Network Look at Lexical Stress in English Words?
Itai Allouche, Itay Asael, Rotem Rousso +5
Despite their success in speech processing, neural networks often operate as black boxes, prompting the question: what informs their decisions, and how can we interpret them? This…
Tradition or Innovation: A Comparison of Modern ASR Methods for Forced Alignment
Rotem Rousso, Eyal Cohen, Joseph Keshet +1
Forced alignment (FA) plays a key role in speech research through the automatic time alignment of speech signals with corresponding text transcriptions. Despite the move towards en…