When a patient using a breathing support system such as a portable oxygen concentrator (POC) talks, the flow of oxygen to the lungs is disturbed. The ideal moment to administer oxygen-rich air during talking would be the brief inhale moments between utterances. However, the detection of the inhale moment is difficult and the latency of the air transfer from the pump, through the hose, to the nasal cannula may be larger than the inspiratory period in normal speech. The prediction of the next inhale moment could be used to compensate the latency and therefore provide a significantly better support for a patient who needs oxygen-rich air but want to communicate normally. In this paper we provide the first evidence that it is possible to forecasts the next inhale moment from the speech of the talker using deep learning techniques. Breathing forecasting from speech has also other potential applications that we briefly discuss in the paper.

Respiration is a primary process involved in speech production. We can often hear if a person has respiratory difficulty, thus making speech a good pathological indicator for respiratory conditions. This is more relevant to conditions like chronic obstructive pulmonary disease (COPD). Patients with COPD suffer from voice changes with respect to the healthy population. Medical professionals observe that the speech of COPD patients during stable periods differs from the speech during exacerbation. In this paper, we investigate this detection of COPD exacerbation from speech in three approaches: acoustic features identification using a statistical approach, low-level descriptive features with classification, and speech breathing models based on deep learning architectures to estimate the patients’ breathing rate. Our analysis indicates that each of these approaches indeed results in a clear distinction of speech during exacerbation and stable periods of COPD.

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.

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.

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, x-vector and ComParE features are explored. Results are obtained on systems trained on the DementiaBank dataset and tested on an in-house 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.

Respiration is an essential and primary mechanism for speech production. We first inhale and then produce speech while exhaling. When we run out of breath, we stop speaking and inhale. Though this process is involuntary, speech production involves a systematic outflow of air during exhalation characterized by linguistic content and prosodic factors of the utterance. Thus speech and respiration are closely related, and modeling this relationship makes sensing respiratory dynamics directly from the speech plausible, however is not well explored. In this article, we conduct a comprehensive study to explore techniques for sensing breathing signal and breathing parameters from speech using deep learning architectures and address the challenges involved in establishing the practical purpose of this technology. Estimating the breathing pattern from the speech would give us information about the respiratory parameters, thus enabling us to understand the respiratory health using one's speech.

Shortness of breath, or dyspnea is a condition of the cardio-pulmonary 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.

Breathing is the primary mechanism for maintaining the subglottal pressure for speech production. Speech can be seen as a systematic outflow of air during exhalation characterized by linguistic content and prosodic factors. Thus, sensing respiratory dynamics from the speech is plausible. In this paper, we explore techniques for sensing breathing from speech using deep learning architectures including multi-task learning approaches. Estimating the breathing pattern from the speech would give us information about the respiration rate, breathing capacity and thus enable us to understand the pathological condition of a person using one's speech. Training and evaluation of our model on our database of breathing signal and speech for 40 subjects yielded a sensitivity of 0.88 for breath event detection and 5.6 % error for breathing rate estimation.

Contains fulltext : 228158.pdf (Publisher’s version ) (Open Access)

Contains fulltext : 214126.pdf (Publisher’s version ) (Open Access)