Search Results for author:
Found 17 papers, 5 papers with code
SNuC: The Sheffield Numbers Spoken Language Corpus
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.
On monoaural speech enhancement for automatic recognition of real noisy speech using mixture invariant training
In this paper, we explore an improved framework to train a monoaural neural enhancement model for robust speech recognition.
Auditory-Based Data Augmentation for End-to-End Automatic Speech Recognition
End-to-end models have achieved significant improvement on automatic speech recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Exploiting Hidden Representations from a DNN-based Speech Recogniser for Speech Intelligibility Prediction in Hearing-impaired Listeners
An accurate objective speech intelligibility prediction algorithms is of great interest for many applications such as speech enhancement for hearing aids.
Unsupervised Uncertainty Measures of Automatic Speech Recognition for Non-intrusive Speech Intelligibility Prediction
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
Leveraging Bitstream Metadata for Fast, Accurate, Generalized Compressed Video Quality Enhancement
Video compression is a central feature of the modern internet powering technologies from social media to video conferencing.
Teacher-Student MixIT for Unsupervised and Semi-supervised Speech Separation
The proposed method first uses mixtures of unseparated sources and the mixture invariant training (MixIT) criterion to train a teacher model.
DHASP: Differentiable Hearing Aid Speech Processing
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
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.
Time-Domain Speech Extraction with Spatial Information and Multi Speaker Conditioning Mechanism
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.
On End-to-end Multi-channel Time Domain Speech Separation in Reverberant Environments
To reduce the influence of reverberation on spatial feature extraction, a dereverberation pre-processing method has been applied to further improve the separation performance.
CHiME-6 Challenge:Tackling Multispeaker Speech Recognition for Unsegmented Recordings
Following the success of the 1st, 2nd, 3rd, 4th and 5th CHiME challenges we organize the 6th CHiME Speech Separation and Recognition Challenge (CHiME-6).
SDCNet: Video Prediction Using Spatially-Displaced Convolution
We present an approach for high-resolution video frame prediction by conditioning on both past frames and past optical flows.
SDC-Net: Video prediction using spatially-displaced convolution
We present an approach for high-resolution video frame prediction by conditioning on both past frames and past optical flows.
Ranked #1 on
Video Prediction
on YouTube-8M
DNN driven Speaker Independent Audio-Visual Mask Estimation for Speech Separation
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.
The fifth 'CHiME' Speech Separation and Recognition Challenge: Dataset, task and baselines
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
In this work we introduce malware detection from raw byte sequences as a fruitful research area to the larger machine learning community.
