This paper looks in more detail at the Interspeech 2019 computational paralinguistics challenge on the prediction of sleepiness ratings from speech. In this challenge, teams were asked to train a regression model to predict sleepiness from samples of the Düsseldorf Sleepy Language Corpus (DSLC). This challenge was notable because the performance of all entrants was uniformly poor, with even the winning system only achieving a correlation of r=0.37. We look at whether the task itself is achievable, and whether the corpus is suited to training a machine learning system for the task. We perform a listening experiment using samples from the corpus and show that a group of human listeners can achieve a correlation of r=0.7 on this task, although this is mainly by classifying the recordings into one of three sleepiness groups. We show that the corpus, because of its construction, confounds variation with sleepiness and variation with speaker identity, and this was the reason that machine learning systems failed to perform well. We conclude that sleepiness rating prediction from voice is not an impossible task, but that good performance requires more information about sleepy speech and its variability across listeners than is available in the DSLC corpus.

Detection of Amyotrophic Lateral Sclerosis (ALS) via Acoustic Analysis.

In this paper, a large Hungarian spoken language database is introduced. This phonetically-based multi-purpose database contains various types of spontaneous and read speech from 333 monolingual speakers (about 50 minutes of speech sample per speaker). This study presents the background and motivation of the development of the BEA Hungarian database, describes its protocol and the transcription procedure, and also presents existing and proposed research using this database. Due to its recording protocol and the transcription it provides a challenging material for various comparisons of segmental structures of speech also across languages.

This paper reviews the applied Deep Learning (DL) practices in the field of Speaker Recognition (SR), both in verification and identification. Speaker Recognition has been a widely used topic of speech technology. Many research works have been carried out and little progress has been achieved in the past 5–6 years. However, as Deep Learning techniques do advance in most machine learning fields, the former state-of-the-art methods are getting replaced by them in Speaker Recognition too. It seems that Deep Learning becomes the now state-of-the-art solution for both Speaker Verification (SV) and identification. The standard x-vectors, additional to i-vectors, are used as baseline in most of the novel works. The increasing amount of gathered data opens up the territory to Deep Learning, where they are the most effective.