2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDSPE-57.4
Paper Title DEEP NEURAL NETWORK EMBEDDINGS FOR THE ESTIMATION OF THE DEGREE OF SLEEPINESS
Authors José Vicente Egas-López, Gábor Gosztolya, University of Szeged, Hungary
SessionSPE-57: Speech, Depression and Sleepiness
LocationGather.Town
Session Time:Friday, 11 June, 14:00 - 14:45
Presentation Time:Friday, 11 June, 14:00 - 14:45
Presentation Poster
Topic Speech Processing: [SPE-ANLS] Speech Analysis
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Estimating the degree of sleepiness from the human speech is an emerging research problem with straightforward applications. In this study, we employ the x-vector approach, currently the state-of-the-art in speaker recognition, as a neural network feature extractor to detect the level of sleepiness of a speaker. Besides using different corpora for fitting the x-vector DNN, we also experiment with adding noise and reverberation to the training samples. According to our experimental results for the publicly available Dusseldorf Sleepy Language Corpus, utilizing x-vector embeddings as features for Support Vector Regression consistently leads to competitive performance scores in sleepiness detection. In particular, we present the highest Spearman's correlation coefficient on the public corpus that was achieved by a single method.