With recent advances in machine-learning techniques for automatic speech analysis (ASA)-
the computerized extraction of information from speech signals-there is a greater need for …

Data generation and labeling are usually an expensive part of learning for robotics. While
active learning methods are commonly used to tackle the former problem, preference-based …

Data collection and labeling is one of the main challenges in employing machine learning
algorithms in a variety of real-world applications with limited data. While active learning …

Given a large un-transcribed corpus of speech utterances, we address the problem of how to
select a good subset for wordlevel transcription under a given fixed transcription budget. We …

In training speech recognition systems, labeling audio clips can be expensive, and not all
data is equally valuable. Active learning aims to label only the most informative samples to …

We conduct a comparative study on selecting subsets of acoustic data for training phone
recognizers. The data selection problem is approached as a constrained submodular …

Automatic labeling is a type of classification problem. Classification has been studied with
the help of statistical methods for a long time. With the explosion of new better computer …

With the wide adoption of automated speech recognition (ASR) systems, it is increasingly
important to test and improve ASR systems. However, collecting and executing speech test …

Data generation and labeling are often expensive in robot learning. Preference-based
learning is a concept that enables reliable labeling by querying users with preference …

Recently developed end-to-end (E2E) automatic speech recognition (ASR) systems demand
abundance of transcribed speech data, there are several scenarios where the labeling of …