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1

Parametric analysis of pilot voice signals in Parkinson’s disease diagnostics

Majda-Zdancewicz Ewelina, Potulska-Chromik Anna, Nojszewska Monika, Kostera-Pruszczyk Anna

Journal of Automation, Electronics and Electrical Engineering

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2022

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Vol. 4, No. 1

21--28

EN

Parkinson's disease (PD) is a neurodegenerative disease of the central nervous system (CNS) characterized by the progressive loss of dopaminergic neurons in the substantia nigra. The article describes an analysis of pilot voice signal analysis in Parkinson's disease diagnostics. Frequency domain signal analysis was mainly used to assess the state of a patient's voice apparatus in order to support PD diagnostics. The recordings covered uttering the “a” sound at least twice with extended phonation. The research utilized real recordings acquired in the Department of Neurology at the Medical University of Warsaw, Poland. Spectral speech signal coefficients may be determined based on different defined frequency scales. The authors used four frequency scales: linear, Mel, Bark and ERB . Spectral descriptors have been defined for each scales which are widely used in machine and deep learning applications, and perceptual analysis. The usefulness of extracted features was assessed taking into account various methods. The discriminatory ability of individual coefficients was evaluated using the Fisher coefficient and LDA technique.. The results of numerical experiments have shown different efficiencies of the proposed descriptors using different frequencies scales.

PL

Choroba Parkinsona (PD) jest neurodegeneracyjną chorobą ośrodkowego układu nerwowego charakteryzującą się postępującą utratą neuronów dopaminergicznych w istocie czarnej. W artykule opisano analizę rejestracji pilotażowych sygnałów głosu w diagnostyce choroby Parkinsona. Rejestracji podlegało co najmniej dwukrotnie wypowiadanie głoski "a” o przedłużonej fonacji. Do badań wykorzystano nagrania zarejestrowane w Katedrze i Klinice Neurologii Warszawskiego Uniwersytetu Medycznego w Warszawie. Do oceny stanu aparatu głosu pacjenta celem wsparcia diagnostyki choroby Parkinsona wykorzystano w głównej mierze analizę sygnału w dziedzinie częstotliwości. Autorzy zastosowali cztery skale częstości: liniową, skalę typu Mel, skalę typu Bark oraz skalę typu ERB. Dla każdej z tych skali zdefiniowali deskryptory spektralne szeroko stosowane w aplikacjach uczenia maszynowego i głębokiego uczenia się oraz w analizie percepcyjnej. Ocena przydatności wyekstrahowanych cech została zrealizowana z uwzględnieniem różnych metod. Wykorzystano metodą oceny jakości cech przy użyciu współczynnika istotności Fischera oraz analizę LDA. Wyniki eksperymentów numerycznych wykazały różne wydajności proponowanych deskryptorów przy użyciu różnych skal częstości.

2

Speech Emotion Recognition Based on Voice Fundamental Frequency

Dimitrova-Grekow Teodora, Klis Aneta, Igras-Cybulska Magdalena

Archives of Acoustics

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2019

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Vol. 44, No. 2

277--286

EN

The human voice is one of the basic means of communication, thanks to which one also can easily convey the emotional state. This paper presents experiments on emotion recognition in human speech based on the fundamental frequency. AGH Emotional Speech Corpus was used. This database consists of audio samples of seven emotions acted by 12 different speakers (6 female and 6 male). We explored phrases of all the emotions – all together and in various combinations. Fast Fourier Transformation and magnitude spectrum analysis were applied to extract the fundamental tone out of the speech audio samples. After extraction of several statistical features of the fundamental frequency, we studied if they carry information on the emotional state of the speaker applying different AI methods. Analysis of the outcome data was conducted with classifiers: K-Nearest Neighbours with local induction, Random Forest, Bagging, JRip, and Random Subspace Method from algorithms collection for data mining WEKA. The results prove that the fundamental frequency is a prospective choice for further experiments.

3

Emotion recognition from natural speech – emotional profiles

Sapiński T., Kamińska D.

Logistyka

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2015

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nr 4

9712--9721, CD3

EN

Emotion recognition system can improve customer service especially in the case of call centers. Knowledge of the emotional state of the speaker would allow the operator to adapt better and generally improve cooperation. Research in emotion recognition focuses primarily on speech analysis. Emotion classification algorithms designed for real-world application must be able to interpret the emotional content of an utterance or dialog beyond various limitation i.e. speaker, context, personality or culture. This paper presents research on emotion recognition system of spontaneous voice stream based on a multimodal classifier. Experiments were carried out basing on natural speech characterized by seven emotional states. The process of multimodal classification was based on Plutchik’s theory of emotion and emotional profiles.

4

Silent Calls – Causes and Measurements

Baran K., Cegłowski P., Kula S.

Journal of Telecommunications and Information Technology

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2015

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nr 1

20--22

EN

The quality of telephone services is very important from either operator or subscriber point of view. One of the negative phenomenon which affects quality of telephone services is lack of speech signal during a call. This situation occurs relatively frequently in mobile telephony, and is called silent call (SC). Lack of speech signal can occur only once or many times during the call, and degrade connection quality. In this paper, an analysis of this phenomenon is presented. The research base are the results of measurements mobile network one of operators in Trójmiasto a large urban area consisting of three cities: Gdańsk, Gdynia, and Sopot. To estimate impact of silent calls on speech quality, mean opinion score index was calculated using POLQA algorithm.