Catégorie d'événements : SRPP

SRPP: Phonetics and phonology of prenasalized consonants in Mbugu

Didier Demolin, Angélique Amelot, Michael Karani & Maarten Mous

This presentation will focus on the phonetics and phonology of prenasalized consonants in Mbugu and on an exemplary case study of this phenomenon for voiceless prenasalized of this language. This research is based on a field study carried out in March 2024 as part of the Sysori project (LPP, Sorbonne nouvelle, LPL Aix-en-Provence & University of Dar es Salaam). One of the aims of this project is to gather new instrumental data to understand phonetic phenomena specific to the languages of the Rift Valley in East Africa. The aim is to describe precisely physical and biomechanical aspects of specific sounds in order to improve the understanding of diachronic phenomena in the sound systems of this region’s languages. Two other objectives are to contribute to the understanding of certain fundamental theoretical issues (speech timing and articulatory dynamics) and to use new phonetic tools for fieldwork.

Mbugu is a mixed language (Bantu and Cushitic) whose sound system has features common to Bantu (prenasalized consonants) and specific to Cushitic (lateral fricatives, palatal consonants). The study of the Mbugu sound system (which also includes voiceless nasals) is important for understanding the effects of the emergence of a mixed language on the sounds used, modified and borrowed. A number of East African languages devoice the nasal part of prenasalized consonants. These include Nyamwezi and Sukuma, Schadeberg (1992) and Maddieson (1991) and Pokomo, Huffman & Hinnebusch (1998), as well as the mixed/bantu language Ma’á/mbugu, Mous (1993).

The data come from recordings made with 8 Mbugu speakers (2 females and 6 males) combining aerodynamic, acoustic and EGG measurements in a set of 86 words including all prenasalized consonants that appear mainly, but not exclusively, at word initial position in class 9/10 nouns. There are also examples of prenasalized consonants internal to the word such as: kimhpanga ‘falcon’ (IMb+NMb) [internal IMb Mbugu & NMb mbgu normal], kufúnhtu ‘pour’ (IMb), kuʔonhti ‘wash’ (IMb), kuhunhta ‘blow’ (NMb), Irenhte name of a village (I+NMb).

Data were acquired with the AeroMask, designed to simultaneously record oral and nasal airflow, intraoral pressure for labial sounds, EGG and acoustic signal. The mask separates oral and nasal airflow and records intraoral pressure for labial sounds using a tube set between the lips. The special feature of the mask is that it is aerodynamically opaque and acoustically transparent, enabling the acoustic signal to be recorded without additional distortion or resonance. Our presentation will detail this acquisition system and the Matlab script designed to acquire and process the data.

The aerodynamic and acoustic study of the voiceless prenasalized consonants of Mbugu confirms the observations of Mous (1993). Voiceless prenasalized consonants show greater variation than their voiced counterparts. The stop oral part may not be realized, but then manifests itself as a kind of aspiration at the end of the nasal. The nasal part can be either voiceless or voiced. Aerodynamic recordings show a significant nasal airflow when the nasal is identified as voiceless. The EGG confirms that there is no vibration of the vocal folds in this case. In our data, female speakers seem to show greater variation for nasals than males. Prenasalized voiced consonants are always realized with a short oral part at the end of the nasal showing a clear explosion.

Questions relating to the speech timing model proposed by Turk & Shattuck-Huffnagel (2020) will be discussed on the basis of our results.

References

Huffman, Marie K. and Thomas J. Hinnebush 1998. The phonetic nature of “voiceless” nasals in Pokomo: Implications for sound change. Journal of African Languages and Linguistics 19: 1-19.

Maddieson, Ian 1991. Articulatory phonology and Sukuma ‘aspirated nasals’. In Kathleen Hubbard (ed.) Proceedings of the 17th annual meeting of the Berkeley Linguistic Society, Special session on African language structures, pp. 145-54. Berkeley: Berkeley Linguistic Society.

Maganga, Clement and Thilo C. Schadeberg 1992. Kinyamwezi Grammar, Texts, Vocabulary. Cologne: Rüdiger Köppe.

Mous, Maarten 2003. The making of a mixed language: The case of Ma’á/Mbugu. Amsterdam: John Benjamins.

Turk, Alice and Shattuck-Hufnagel Stéphanie. 2020. Speech Timing. Implications for Theories of Phonology, phonetics, and Speech Motor Control. Oxford, Oxford University Press.

SRPP: A gentle introduction to vocal biomarkers of motor speech disorders

This presentation aims to guide you through the fundamentals of acoustic vocal biomarkers in motor speech disorders and demonstrate how it can be useful to phoneticians.
The data included 570 native Czech speakers, including 42 subjects with idiopathic rapid eye movement sleep behavior disorder (RBD), 32 with early, untreated Parkinson’s disease (PDU), 26 with treated Parkinson’s disease (PDT), 22 with multiple system atrophy (MSA), 15 with progressive supranuclear palsy (PSP), 18 with untreated Huntington’s disease (HDU), 13 with treated Huntington’s disease (HDT), 17 with cerebellar ataxia (CA), 101 with multiple sclerosis (MS), and 284 healthy controls (HC) with no neurological or communication disorders. Please see Hlavnička (2018a) for more details about the cohort. Each speaker performed tasks that included sustained vowels (/A/ and /I/), a rhythm test, reading, a monologue, and a diadochokinetic task. The recordings were made in a quiet, non-reverberant room using a headset condenser omnidirectional microphone.
I analyzed speech features related to prosody, articulation, resonance, phonation, and respiration calculated via the Dysarthria Analyzer software (https://www.dysan.cz/, Hlavnička 2018b). To allow comparisons, all measured values were normalized. A subgroup of healthy controls, matched for age and sex with the individuals in each clinical group, was used to calculate z-scores. These z-scores were compared across all groups using one-way ANOVA for normally distributed features, and Kruskal-Wallis tests for gamma-distributed features. The significance level was set at 0.05.
Additionally, I conducted a classification experiment to identify hypokinetic and hyperkinetic speech tendencies (characterized by reduced movement amplitude) and excitatory speech tendencies (characterized by exaggerated movements due to lack of coordination or involuntary muscle contractions).
Among healthy controls (HC), only a small number exhibited significant hypokinetic (4%), hyperkinetic (2%), or unspecific (2%) speech tendencies. Hypokinetic speech patterns were most common in PDU (56%), PDT (67%), MSA (64%), PSP (34%), and less so in RBD subjects (18%), who are at high risk for developing Parkinson’s disease (PD). Hypokinetic tendencies were also observed in MS (23%), CA (15%), HDT (10%), and HDU (18%), often alongside hyperkinetic patterns. Hyperkinetic speech tendencies were particularly frequent in MS (31%), CA (31%), and most notably in HDU (73%) and HDT (100%). A significant increase in hyperkinetic tendencies was found in MSA (28%) compared to PSP (7%, z-test, p < 0.01).
Tendencies to hypokinesia and hyperkinesia were found to be consistent with underlying hypotheses about disease manifestations. Observed clusters could have diagnostic value for recognizing Parkinson’s disease (PD) and atypical parkinsonian syndromes (APS). The results suggests that acoustic signs may be more distinct in early stages, before broader speech impairments occur due to speaker’s compensation strategies. Factors like patient history, oral exams, and perceptual findings should be considered individually to assess whether a feature serves as a disease marker or indicates progression.
Additionally, applicability of vocal biomarkers on other languages will be demonstrated on a large cohort including Czech, German, English, French, and Italian language collected within the multicentric international project (Rusz et al. 2021).
This analysis has been developed into a comprehensive methodology available at https://www.dysan.cz. A free e-mail course on how to start with vocal biomarkers of motor speech disorders is available at https://events.vocalgebra.com/first5speakers. Please feel free to contact the author at jan.hlavnicka@dysan.cz.
References
Hlavnička J. (2018a). Automated analysis of speech disorders in neurodegenerative diseases. Doctoral Thesis. Czech Technical University in Prague: Prague, Czech Republic.
Hlavnička J. (2018b). The Dysarthria Analyzer: computer software. Available at https://www.dysan.cz/.
Rusz J, Hlavnička J, Novotný M, Tykalová T, Pelletier A, Montplaisir J, Gagnon JF, Dušek P, Galbiati A, Marelli S, Timm PC, Teigen LN, Janzen A, Habibi M, Stefani A, Holzknecht E, Seppi K, Evangelista E, Rassu AL, Dauvilliers Y, Högl B, Oertel W, St Louis EK, Ferini-Strambi L, Růžička E, Postuma RB, Šonka K. (2021). Speech biomarkers in rapid eye movement sleep behavior disorder and Parkinson disease. Annals of neurology, 90(1), 62-75.