The intensities of vowels and plosive bursts and their impact on text intelligibility in singinga).

In classical singing, there are often problems with the intelligibility of sung text. The present study aims to test the hypotheses that (1) in loud operatic singing, compared with speaking, the intensity of voiceless plosives increases less than the intensity of vowels, leading to poorer recognition of plosives; and (2) pronouncing the plosive bursts with greater intensity improves their recognition. The acoustic analysis of nine opera arias in Italian from the Classical and Romantic periods performed by ten classically trained singers showed that the average difference in the intensity of vowels when sung and spoken was 14.6 dB [standard deviation (SD) = 7.2 dB], while the difference in the intensity of voiceless plosive bursts was only 6.6 dB (SD = 6 dB). In a perception test with 73 participants, increasing the intensity of the plosive bursts generally improved the recognition of plosives in the sung /a-plosive-a/ sequences, but mainly when reverberation and/or pink noise imitating instrumental accompaniments were added to the stimuli. At the same time, recognition of plosives was often better than chance even when the plosive burst was missing and replaced by silence.

Detection of Bulbar Involvement in Patients With Amyotrophic Lateral Sclerosis by Machine Learning Voice Analysis: Diagnostic Decision Support Development Study.

BACKGROUND: Bulbar involvement is a term used in amyotrophic lateral sclerosis (ALS) that refers to motor neuron impairment in the corticobulbar area of the brainstem, which produces a dysfunction of speech and swallowing. One of the earliest symptoms of bulbar involvement is voice deterioration characterized by grossly defective articulation; extremely slow, laborious speech; marked hypernasality; and severe harshness. Bulbar involvement requires well-timed and carefully coordinated interventions. Therefore, early detection is crucial to improving the quality of life and lengthening the life expectancy of patients with ALS who present with this dysfunction. Recent research efforts have focused on voice analysis to capture bulbar involvement. OBJECTIVE: The main objective of this paper was (1) to design a methodology for diagnosing bulbar involvement efficiently through the acoustic parameters of uttered vowels in Spanish, and (2) to demonstrate that the performance of the automated diagnosis of bulbar involvement is superior to human diagnosis. METHODS: The study focused on the extraction of features from the phonatory subsystem-jitter, shimmer, harmonics-to-noise ratio, and pitch-from the utterance of the five Spanish vowels. Then, we used various supervised classification algorithms, preceded by principal component analysis of the features obtained. RESULTS: To date, support vector machines have performed better (accuracy 95.8%) than the models analyzed in the related work. We also show how the model can improve human diagnosis, which can often misdiagnose bulbar involvement. CONCLUSIONS: The results obtained are very encouraging and demonstrate the efficiency and applicability of the automated model presented in this paper. It may be an appropriate tool to help in the diagnosis of ALS by multidisciplinary clinical teams, in particular to improve the diagnosis of bulbar involvement.