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Found 26 papers, 2 papers with code
TB or not TB? Acoustic cough analysis for tuberculosis classification
In this work, we explore recurrent neural network architectures for tuberculosis (TB) cough classification.
Automatic Tuberculosis and COVID-19 cough classification using deep learning
This cough data include 1. 68 hours of TB coughs, 18. 54 minutes of COVID-19 coughs and 1. 69 hours of healthy coughs from 47 TB patients, 229 COVID-19 patients and 1498 healthy patients and were used to train and evaluate a CNN, LSTM and Resnet50.
Accelerometer-based Bed Occupancy Detection for Automatic, Non-invasive Long-term Cough Monitoring
When integrated into a complete cough monitoring system, the daily cough rate of a patient undergoing TB treatment was determined over a period of 14 days.
Wake-Cough: cough spotting and cougher identification for personalised long-term cough monitoring
We present `wake-cough', an application of wake-word spotting to coughs using a Resnet50 and the identification of coughers using i-vectors, for the purpose of a long-term, personalised cough monitoring system.
Automatic non-invasive Cough Detection based on Accelerometer and Audio Signals
We present an automatic non-invasive way of detecting cough events based on both accelerometer and audio signals.
Feature learning for efficient ASR-free keyword spotting in low-resource languages
We show that, using these features, the CNN-DTW keyword spotter performs almost as well as the DTW keyword spotter while outperforming a baseline CNN trained only on the keyword templates.
Multilingual training set selection for ASR in under-resourced Malian languages
We find that, although maximising the training pool by including all six additional languages provides improved speech recognition in both target languages, substantially better performance can be achieved by a more judicious choice.
COVID-19 Detection in Cough, Breath and Speech using Deep Transfer Learning and Bottleneck Features
We present an experimental investigation into the effectiveness of transfer learning and bottleneck feature extraction in detecting COVID-19 from audio recordings of cough, breath and speech.
Automatic Cough Classification for Tuberculosis Screening in a Real-World Environment
Objective: The automatic discrimination between the coughing sounds produced by patients with tuberculosis (TB) and those produced by patients with other lung ailments.
A Hybrid CNN-BiLSTM Voice Activity Detector
We find that significantly smaller models with near optimal parameters perform on par with larger models trained with optimal parameters.
Deep Neural Network based Cough Detection using Bed-mounted Accelerometer Measurements
Since the need to gather audio is avoided and therefore privacy is inherently protected, and since the accelerometer is attached to the bed and not worn, this form of monitoring may represent a more convenient and readily accepted method of long-term patient cough monitoring.
COVID-19 Cough Classification using Machine Learning and Global Smartphone Recordings
We present a machine learning based COVID-19 cough classifier which can discriminate COVID-19 positive coughs from both COVID-19 negative and healthy coughs recorded on a smartphone.
Multilingual Bottleneck Features for Improving ASR Performance of Code-Switched Speech in Under-Resourced Languages
In this work, we explore the benefits of using multilingual bottleneck features (mBNF) in acoustic modelling for the automatic speech recognition of code-switched (CS) speech in African languages.
Semi-supervised acoustic and language model training for English-isiZulu code-switched speech recognition
We present an analysis of semi-supervised acoustic and language model training for English-isiZulu code-switched (CS) ASR using soap opera speech.
Semi-supervised Acoustic Modelling for Five-lingual Code-switched ASR using Automatically-segmented Soap Opera Speech
Automatic segmentation was applied in combination with automaticspeaker diarization.
Semi-supervised Development of ASR Systems for Multilingual Code-switched Speech in Under-resourced Languages
This paper reports on the semi-supervised development of acoustic and language models for under-resourced, code-switched speech in five South African languages.
Improved low-resource Somali speech recognition by semi-supervised acoustic and language model training
The automatic transcriptions from the best performing pass were used for language model augmentation.
Semi-supervised acoustic model training for five-lingual code-switched ASR
Furthermore, because English is common to all language pairs in our data, it dominates when training a unified language model, leading to improved English ASR performance at the expense of the other languages.
Feature exploration for almost zero-resource ASR-free keyword spotting using a multilingual bottleneck extractor and correspondence autoencoders
We compare features for dynamic time warping (DTW) when used to bootstrap keyword spotting (KWS) in an almost zero-resource setting.
Cluster Size Management in Multi-Stage Agglomerative Hierarchical Clustering of Acoustic Speech Segments
Agglomerative hierarchical clustering (AHC) requires only the similarity between objects to be known.
Feature Trajectory Dynamic Time Warping for Clustering of Speech Segments
Dynamic time warping (DTW) can be used to compute the similarity between two sequences of generally differing length.
Building a Unified Code-Switching ASR System for South African Languages
We present our first efforts towards building a single multilingual automatic speech recognition (ASR) system that can process code-switching (CS) speech in five languages spoken within the same population.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
ASR-free CNN-DTW keyword spotting using multilingual bottleneck features for almost zero-resource languages
We consider multilingual bottleneck features (BNFs) for nearly zero-resource keyword spotting.
Automatic Speech Recognition for Humanitarian Applications in Somali
We present our first efforts in building an automatic speech recognition system for Somali, an under-resourced language, using 1. 57 hrs of annotated speech for acoustic model training.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Fast ASR-free and almost zero-resource keyword spotting using DTW and CNNs for humanitarian monitoring
While the resulting CNN keyword spotter cannot match the performance of the DTW-based system, it substantially outperforms a CNN classifier trained only on the keywords, improving the area under the ROC curve from 0. 54 to 0. 64.
