Automatic depression severity assessment with deep learning using parameter-efficient tuning.

Introduction: To assist mental health care providers with the assessment of depression, research to develop a standardized, accessible, and non-invasive technique has garnered considerable attention. Our study focuses on the application of deep learning models for automatic assessment of depression severity based on clinical interview transcriptions. Despite the recent success of deep learning, the lack of large-scale high-quality datasets is a major performance bottleneck for many mental health applications.

Modeling the effect of linguistic predictability on speech intelligibility prediction.

Many existing speech intelligibility prediction (SIP) algorithms can only account for acoustic factors affecting speech intelligibility and cannot predict intelligibility across corpora with different linguistic predictability. To address this, a linguistic component was added to five existing SIP algorithms by estimating linguistic corpus predictability using a pre-trained language model. The results showed improved SIP performance in terms of correlation and prediction error over a mixture of four datasets, each with a different English open-set corpus.

Spectro-temporal modulation glimpsing for speech intelligibility prediction.

We compare two alternative speech intelligibility prediction algorithms: time-frequency glimpse proportion (GP) and spectro-temporal glimpsing index (STGI). Both algorithms hypothesize that listeners understand speech in challenging acoustic environments by "glimpsing" partially available information from degraded speech. GP defines glimpses as those time-frequency regions whose local signal-to-noise ratio is above a certain threshold and estimates intelligibility as the proportion of the time-frequency regions glimpsed.

Use of Machine Learning and Artificial Intelligence Methods in Geriatric Mental Health Research Involving Electronic Health Record or Administrative Claims Data: A Systematic Review.

Electronic health records (EHR) and administrative healthcare data (AHD) are frequently used in geriatric mental health research to answer various health research questions. However, there is an increasing amount and complexity of data available that may lend itself to alternative analytic approaches using machine learning (ML) or artificial intelligence (AI) methods. We performed a systematic review of the current application of ML or AI approaches to the analysis of EHR and AHD in geriatric mental health.

Speech Intelligibility Prediction using Spectro-Temporal Modulation Analysis.

Spectro-temporal modulations are believed to mediate the analysis of speech sounds in the human primary auditory cortex. Inspired by humans' robustness in comprehending speech in challenging acoustic environments, we propose an intrusive speech intelligibility prediction (SIP) algorithm, wSTMI, for normal-hearing listeners based on spectro-temporal modulation analysis (STMA) of the clean and degraded speech signals. In the STMA, each of 55 modulation frequency channels contributes an intermediate intelligibility measure.

The effect of simulated room acoustic parameters on the intelligibility and perceived reverberation of monosyllabic words and sentences.

Individual acoustic parameters of reverberation have the potential to affect both the intelligibility of speech and the degree of perceived reverberation. The current experiments used monaural acoustic simulations to investigate the effect of reverberation time (RT) and direct-to-reverberant ratio (DRR) on word and sentence intelligibility at different levels of analysis (phonemes, words, and sentences). Perceived reverberation and recall of sentences were also assessed.

Reference-free automatic quality assessment of tracheoesophageal speech.

Evaluation of the quality of tracheoesophageal (TE) speech using machines instead of human experts can enhance the voice rehabilitation process for patients who have undergone total laryngectomy and voice restoration. Towards the goal of devising a reference-free TE speech quality estimation algorithm, we investigate the efficacy of speech signal features that are used in standard telephone-speech quality assessment algorithms, in conjunction with a recently introduced speech modulation spectrum measure. Tests performed on two TE speech databases demonstrate that the modulation spectral measure and a subset of features in the standard ITU-T P.

Modulation filtering for heart and lung sound separation from breath sound recordings.

Separation of heart and lung sounds from breath sound recordings is a challenging task due to the temporal and spectral overlap of the two signals. In this paper, the use of a spectro-temporal representation to improve signal separation is investigated. The representation is obtained by means of a frequency decomposition (termed modulation frequency) of temporal trajectories of short-term spectral components.

Soft contact lens cleaning: rub or no-rub?

Purpose: To compare effectiveness of cleaning with and without rubbing of soft contact lenses.

Methods: Three-hundred new biweekly disposable hydrogel lenses (Ocufilcon D, FDA Group IV; 55% water content) were artificially deposited with serum albumin, hand cream (semi-transparent deposits) and mascara (black deposits). The treated lenses were randomly divided into three groups, each group cleaned by one of three methods of cleaning--Rubbing (R), No-Rub following the manufacturer's instruction on duration of rinsing (NR1) and No-Rub with a shorter duration of rinsing (NR2).