Sensing of inspiration events from speech:comparison of deep learning and linguistic methods

Respiratory chest belt sensor can be used to measure the respiratory rate and other respiratory health parameters. Virtual Respiratory Belt, VRB, algorithms estimate the belt sensor waveform from speech audio. In this paper we compare the detection of inspiration events (IE) from respiratory belt sensor data using a novel neural VRB algorithm and the detections based on time-aligned linguistic content. The results show the superiority of the VRB method over word pause detection or grammatical content segmentation. The comparison of the methods show that both read and spontaneous speech content has a significant amount of ungrammatical breathing, that is, breathing events that are not aligned with grammatically appropriate places in language. This study gives new insights into the development of VRB methods and adds to the general understanding of speech breathing behavior. Moreover, a new VRB method, VRBOLA, for the reconstruction of the continuous breathing waveform is demonstrated

Sensing of inspiration events from speech:comparison of deep learning and linguistic methods

Respiratory chest belt sensor can be used to measure the respiratory rate and other respiratory health parameters. Virtual Respiratory Belt, VRB, algorithms estimate the belt sensor waveform from speech audio. In this paper we compare the detection of inspiration events (IE) from respiratory belt sensor data using a novel neural VRB algorithm and the detections based on time-aligned linguistic content. The results show the superiority of the VRB method over word pause detection or grammatical content segmentation. The comparison of the methods show that both read and spontaneous speech content has a significant amount of ungrammatical breathing, that is, breathing events that are not aligned with grammatically appropriate places in language. This study gives new insights into the development of VRB methods and adds to the general understanding of speech breathing behavior. Moreover, a new VRB method, VRBOLA, for the reconstruction of the continuous breathing waveform is demonstrated

COVID-19 detection based on respiratory sensing from speech

COVID-19 affects a person's respiratory health, which is manifested in the form of shortness of breath during speech. Recent work shows that it is possible to use deep learning techniques to sense the speaker's respiratory parameters from a speech signal directly. Thus respiratory parameters like speech breathing rate and tidal volume can be computed and compared using deep learning techniques to detect COVID-19 from speech recordings. In this paper, we compute respiratory parameters using our pre-trained deep learning-based speech breathing models and use them for detecting COVID-19 from speech. Apart from using speech breathing models, we perform acoustic features identification using a statistical approach and classification based on low-level descriptive features. Our analysis investigates the distinction of speech of a healthy person and COVID-19 affected person.</p

Deep sensing of breathing signal during conversational speech

In this paper, we show the first results on the estimation of breathing signal from conversational speech using deep learning algorithms. Respiratory diseases such as COPD, asthma, and respiratory infections are common in the elderly population and patients in health care monitoring and medical alert services in general. In this work, we compare algorithms for the estimation of a known respiratory target signal, measured by respiratory belt transducers positioned across the rib cage and abdomen, from conversational speech. We demonstrate the estimation of the respiratory signal from speech using convolutional and recurrent neural networks. The estimated breathing pattern gives respiratory rate, breathing capacity and thus might provide indications of the pathological condition of the speaker. Evaluation of our model on our database of breathing signal and speech yielded a sensitivity of 91.2 % for breath event detection and a mean absolute error of 1.01 breaths per minute for breathing rate estimation.</p

Detection of mild dyspnea from pairs of speech recordings

Shortness of breath, or dyspnea is a condition of the cardiopulmonary system that may be caused by, for example, a heart or lung disease, or physical load. In this paper, we explore techniques of detecting mild dyspnea directly from conversational speech, for example, in a telehealth application. We demonstrate with a collection of speech recordings before and after a light physical exercise that a siamese neural network, when presented examples of the two conditions, can detect the difference between two speech signals. This shows that this signal can be detected using data-pairs, removing the need for ratings of severity or the distinction of separate classes.</p

PHONEME BASED RESPIRATORY ANALYSIS OF READ SPEECH

Recent work shows that it is possible to use deep learning techniques to sense the speaker's respiratory parameters directly from a speech signal. This can be a beneficial option for future telehealth services. In this paper, we dive deeper and study how respiratory effort depends on the linguistic content of the speech utterance. This is obtained by analysis of respiratory belt sensor data and phoneme-aligned speech data. The results show, for example, that the respiratory effort was highest for fricatives, compared to other broad phonetic classes, and especially high for the glottal consonants. The insights may help to develop more efficient protocols for respiratory health monitoring in telehealth applications.</p

Multi-Task Estimation of Age and Cognitive Decline from Speech

Speech is a common physiological signal that can be affected by both ageing and cognitive decline. Often the effect can be confounding, as would be the case for people at, e.g., very early stages of cognitive decline due to dementia. Despite this, the automatic predictions of age and cognitive decline based on cues found in the speech signal are generally treated as two separate tasks. In this paper, multi-task learning is applied for the joint estimation of age and the Mini-Mental Status Evaluation criteria (MMSE) commonly used to assess cognitive decline. To explore the relationship between age and MMSE, two neural network architectures are evaluated: a SincNet-based end-to-end architecture, and a system comprising of a feature extractor followed by a shallow neural network. Both are trained with single-task or multi-task targets. To compare, an SVM-based regressor is trained in a single-task setup. i-vector, xvector and ComParE features are explored. Results are obtained on systems trained on the DementiaBank dataset and tested on an inhouse dataset as well as the ADReSS dataset. The results show that both the age and MMSE estimation is improved by applying multitask learning, with state-of-the-art results achieved on the ADReSS dataset acoustic-only task.</p

On the relationship between speech-based breathing signal prediction evaluation measures and breathing parameters estimation

The respiratory system is one of the major components of the speech production system. Any alteration in breathing can result in changes in speech. Specific breathing characteristics, such as breathing rate and tidal volume, can indicate a person's pathological condition. More recently, neural network-based methods have started emerging for predicting the breathing signal from the speech signal. The neural networks are trained and evaluated with different objective measures, such as mean squared error (MSE) and Pearson's correlation. This paper investigates whether there is a systematic relationship between the different objective measures used for training and evaluating the neural network models and the end-goal, i.e. estimation of breathing parameters such as, breathing rate and tidal volume. Our investigations on two different data sets with two different neural network-based approaches show that there is no clear systematic relationship. In other words, obtaining a high Pearson's correlation on the evaluation set does not necessarily mean better breathing parameter estimation. Thus, indicating the need for developing other objective evaluation measures.</p