5 research outputs found
Fearless Steps Challenge Phase-1 Evaluation Plan
The Fearless Steps Challenge 2019 Phase-1 (FSC-P1) is the inaugural Challenge
of the Fearless Steps Initiative hosted by the Center for Robust Speech Systems
(CRSS) at the University of Texas at Dallas. The goal of this Challenge is to
evaluate the performance of state-of-the-art speech and language systems for
large task-oriented teams with naturalistic audio in challenging environments.
Researchers may select to participate in any single or multiple of these
challenge tasks. Researchers may also choose to employ the FEARLESS STEPS
corpus for other related speech applications. All participants are encouraged
to submit their solutions and results for consideration in the ISCA
INTERSPEECH-2019 special session.Comment: Document Generated in February 2019 for conducting the Fearless Steps
Challenge Phase-1 and its associated ISCA Interspeech-2019 Special Sessio
FEARLESS STEPS Challenge (FS-2): Supervised Learning with Massive Naturalistic Apollo Data
The Fearless Steps Initiative by UTDallas-CRSS led to the digitization,
recovery, and diarization of 19,000 hours of original analog audio data, as
well as the development of algorithms to extract meaningful information from
this multi-channel naturalistic data resource. The 2020 FEARLESS STEPS (FS-2)
Challenge is the second annual challenge held for the Speech and Language
Technology community to motivate supervised learning algorithm development for
multi-party and multi-stream naturalistic audio. In this paper, we present an
overview of the challenge sub-tasks, data, performance metrics, and lessons
learned from Phase-2 of the Fearless Steps Challenge (FS-2). We present
advancements made in FS-2 through extensive community outreach and feedback. We
describe innovations in the challenge corpus development, and present revised
baseline results. We finally discuss the challenge outcome and general trends
in system development across both phases (Phase FS-1 Unsupervised, and Phase
FS-2 Supervised) of the challenge, and its continuation into multi-channel
challenge tasks for the upcoming Fearless Steps Challenge Phase-3.Comment: Paper Accepted in the Interspeech 2020 Conferenc
On the Robustness of Arabic Speech Dialect Identification
Arabic dialect identification (ADI) tools are an important part of the
large-scale data collection pipelines necessary for training speech recognition
models. As these pipelines require application of ADI tools to potentially
out-of-domain data, we aim to investigate how vulnerable the tools may be to
this domain shift. With self-supervised learning (SSL) models as a starting
point, we evaluate transfer learning and direct classification from SSL
features. We undertake our evaluation under rich conditions, with a goal to
develop ADI systems from pretrained models and ultimately evaluate performance
on newly collected data. In order to understand what factors contribute to
model decisions, we carry out a careful human study of a subset of our data.
Our analysis confirms that domain shift is a major challenge for ADI models. We
also find that while self-training does alleviate this challenges, it may be
insufficient for realistic conditions
A survey on artificial intelligence-based acoustic source identification
The concept of Acoustic Source Identification (ASI), which refers to the process of identifying noise sources has attracted increasing attention in recent years. The ASI technology can be used for surveillance, monitoring, and maintenance applications in a wide range of sectors, such as defence, manufacturing, healthcare, and agriculture. Acoustic signature analysis and pattern recognition remain the core technologies for noise source identification. Manual identification of acoustic signatures, however, has become increasingly challenging as dataset sizes grow. As a result, the use of Artificial Intelligence (AI) techniques for identifying noise sources has become increasingly relevant and useful. In this paper, we provide a comprehensive review of AI-based acoustic source identification techniques. We analyze the strengths and weaknesses of AI-based ASI processes and associated methods proposed by researchers in the literature. Additionally, we did a detailed survey of ASI applications in machinery, underwater applications, environment/event source recognition, healthcare, and other fields. We also highlight relevant research directions