7 accepted papers
Automatic Speech Recognition (ASR) systems frequently produce transcription errors due to acoustic variability, which require post-processing correction methods. Recent approaches leverage Large Language Models (LLMs) for generative ASR error correction using N-best hypotheses but rely on fixed set…
We address the problem of uncertainty quantification (UQ) in the localization of a sound source within adverse acoustic environments. Estimating the position of the source is influenced by various factors, such as noise and reverberation, leading to significant uncertainty. Quantifying this uncertai…
Conformal prediction has attracted significant attention as a distribution-free method for uncertainty quantification in black-box models, providing prediction sets with guaranteed coverage. However, its practical utility is often limited when these prediction sets become excessively large, reducing…
Machine learning applications frequently come with multiple diverse objectives and constraints that can change over time. Accordingly, trained models can be tuned with sets of hyper-parameters that affect their predictive behavior (e.g., their run-time efficiency versus error rate). As the number of…
This paper investigates localization of an arbitrary number of simultaneously active speakers in an acoustic enclosure. We propose an algorithm capable of estimating the number of speakers, using reliability information to obtain robust estimation results in adverse acoustic scenarios and estimating…
Despite attracting significant research efforts, the problem of source localization in noisy and reverberant environments remains challenging. Novel learning-based methods attempt to solve the problem by modelling the acoustic environment from the observed data. Typically, appropriate feature vector…
Sound source localization is addressed by a novel Bayesian approach using a data-driven geometric model. The goal is to recover the target function that attaches each acoustic sample, formed by the measured signals, with its corresponding position. The estimation is derived by maximizing the posteri…