Search Results for author: Jon Barker

Found 17 papers, 5 papers with code

SNuC: The Sheffield Numbers Spoken Language Corpus

no code implementations LREC 2022 Emma Barker, Jon Barker, Robert Gaizauskas, Ning Ma, Monica Lestari Paramita

We present SNuC, the first published corpus of spoken alphanumeric identifiers of the sort typically used as serial and part numbers in the manufacturing sector.

Exploiting Hidden Representations from a DNN-based Speech Recogniser for Speech Intelligibility Prediction in Hearing-impaired Listeners

1 code implementation8 Apr 2022 Zehai Tu, Ning Ma, Jon Barker

An accurate objective speech intelligibility prediction algorithms is of great interest for many applications such as speech enhancement for hearing aids.

Speech Enhancement speech-recognition +1

Unsupervised Uncertainty Measures of Automatic Speech Recognition for Non-intrusive Speech Intelligibility Prediction

1 code implementation8 Apr 2022 Zehai Tu, Ning Ma, Jon Barker

Non-intrusive intelligibility prediction is important for its application in realistic scenarios, where a clean reference signal is difficult to access.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

Teacher-Student MixIT for Unsupervised and Semi-supervised Speech Separation

no code implementations15 Jun 2021 Jisi Zhang, Catalin Zorila, Rama Doddipatla, Jon Barker

The proposed method first uses mixtures of unseparated sources and the mixture invariant training (MixIT) criterion to train a teacher model.

Speech Separation

DHASP: Differentiable Hearing Aid Speech Processing

no code implementations15 Mar 2021 Zehai Tu, Ning Ma, Jon Barker

In this paper, we explore an alternative approach to finding the optimal fitting by introducing a hearing aid speech processing framework, in which the fitting is optimised in an automated way using an intelligibility objective function based on the HASPI physiological auditory model.

The Use of Voice Source Features for Sung Speech Recognition

no code implementations20 Feb 2021 Gerardo Roa Dabike, Jon Barker

In this paper, we ask whether vocal source features (pitch, shimmer, jitter, etc) can improve the performance of automatic sung speech recognition, arguing that conclusions previously drawn from spoken speech studies may not be valid in the sung speech domain.

speech-recognition Speech Recognition

Time-Domain Speech Extraction with Spatial Information and Multi Speaker Conditioning Mechanism

no code implementations7 Feb 2021 Jisi Zhang, Catalin Zorila, Rama Doddipatla, Jon Barker

In this paper, we present a novel multi-channel speech extraction system to simultaneously extract multiple clean individual sources from a mixture in noisy and reverberant environments.

Speech Extraction speech-recognition +1

On End-to-end Multi-channel Time Domain Speech Separation in Reverberant Environments

no code implementations11 Nov 2020 Jisi Zhang, Catalin Zorila, Rama Doddipatla, Jon Barker

To reduce the influence of reverberation on spatial feature extraction, a dereverberation pre-processing method has been applied to further improve the separation performance.

speech-recognition Speech Recognition +1

SDCNet: Video Prediction Using Spatially-Displaced Convolution

1 code implementation2 Nov 2018 Fitsum A. Reda, Guilin Liu, Kevin J. Shih, Robert Kirby, Jon Barker, David Tarjan, Andrew Tao, Bryan Catanzaro

We present an approach for high-resolution video frame prediction by conditioning on both past frames and past optical flows.

Optical Flow Estimation SSIM +1

DNN driven Speaker Independent Audio-Visual Mask Estimation for Speech Separation

no code implementations31 Jul 2018 Mandar Gogate, Ahsan Adeel, Ricard Marxer, Jon Barker, Amir Hussain

The process of selective attention in the brain is known to contextually exploit the available audio and visual cues to better focus on target speaker while filtering out other noises.

Speech Separation

The fifth 'CHiME' Speech Separation and Recognition Challenge: Dataset, task and baselines

no code implementations28 Mar 2018 Jon Barker, Shinji Watanabe, Emmanuel Vincent, Jan Trmal

The CHiME challenge series aims to advance robust automatic speech recognition (ASR) technology by promoting research at the interface of speech and language processing, signal processing , and machine learning.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +4

Malware Detection by Eating a Whole EXE

7 code implementations25 Oct 2017 Edward Raff, Jon Barker, Jared Sylvester, Robert Brandon, Bryan Catanzaro, Charles Nicholas

In this work we introduce malware detection from raw byte sequences as a fruitful research area to the larger machine learning community.

Malware Detection

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