Search Results for author:
Found 18 papers, 8 papers with code
RoomReader: A Multimodal Corpus of Online Multiparty Conversational Interactions
The corpus was developed within the wider RoomReader Project to explore multimodal cues of conversational engagement and behavioural aspects of collaborative interaction in online environments.
Language Bias in Self-Supervised Learning For Automatic Speech Recognition
Self-supervised learning (SSL) is used in deep learning to train on large datasets without the need for expensive labelling of the data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Uncovering the Visual Contribution in Audio-Visual Speech Recognition
We first quantify the visual contribution using effective SNR gains at 0 dB and then investigate the use of visual information in terms of its temporal distribution and word-level informativeness.
Noise-Robust Hearing Aid Voice Control
Advancing the design of robust hearing aid (HA) voice control is crucial to increase the HA use rate among hard of hearing people as well as to improve HA users' experience.
Learnable Frontends that do not Learn: Quantifying Sensitivity to Filterbank Initialisation
We show that although performance is overall improved, the filterbanks exhibit strong sensitivity to their initialisation strategy.
Learnable Acoustic Frontends in Bird Activity Detection
Combining this data with species agnostic bird activity detection systems enables the monitoring of activity levels of bird populations.
Bioacoustic Event Detection with prototypical networks and data augmentation
This report presents deep learning and data augmentation techniques used by a system entered into the Few-Shot Bioacoustic Event Detection for the DCASE2021 Challenge.
Low Resource Species Agnostic Bird Activity Detection
This paper explores low resource classifiers and features for the detection of bird activity, suitable for embedded Automatic Recording Units which are typically deployed for long term remote monitoring of bird populations.
AV Taris: Online Audio-Visual Speech Recognition
In recent years, Automatic Speech Recognition (ASR) technology has approached human-level performance on conversational speech under relatively clean listening conditions.
Deep Multi-Scale Feature Learning for Defocus Blur Estimation
This paper presents an edge-based defocus blur estimation method from a single defocused image.
Learning to Count Words in Fluent Speech enables Online Speech Recognition
Sequence to Sequence models, in particular the Transformer, achieve state of the art results in Automatic Speech Recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Should we hard-code the recurrence concept or learn it instead ? Exploring the Transformer architecture for Audio-Visual Speech Recognition
The audio-visual speech fusion strategy AV Align has shown significant performance improvements in audio-visual speech recognition (AVSR) on the challenging LRS2 dataset.
Neural Generation of Dialogue Response Timings
The timings of spoken response offsets in human dialogue have been shown to vary based on contextual elements of the dialogue.
How to Teach DNNs to Pay Attention to the Visual Modality in Speech Recognition
A recently proposed multimodal fusion strategy, AV Align, based on state-of-the-art sequence to sequence neural networks, attempts to model this relationship by explicitly aligning the acoustic and visual representations of speech.
Attention-based Audio-Visual Fusion for Robust Automatic Speech Recognition
Automatic speech recognition can potentially benefit from the lip motion patterns, complementing acoustic speech to improve the overall recognition performance, particularly in noise.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Multimodal Continuous Turn-Taking Prediction Using Multiscale RNNs
To design spoken dialog systems that can conduct fluid interactions it is desirable to incorporate cues from separate modalities into turn-taking models.
Investigating Speech Features for Continuous Turn-Taking Prediction Using LSTMs
The continuous predictions represent generalized turn-taking behaviors observed in the training data and can be applied to make decisions that are not just limited to end-of-turn detection.
Can DNNs Learn to Lipread Full Sentences?
Finding visual features and suitable models for lipreading tasks that are more complex than a well-constrained vocabulary has proven challenging.
