A practical method of estimating the time-varying degree of vowel nasalization from acoustic features

This paper presents a simple and easy-to-use method of creating a time-varying signal of the degree of nasalization in vowels, generated from acoustic features measured in oral and nasalized vowel contexts. The method is presented for separate models constructed using two sets of acoustic features: (1) an uninformed set of 13 Mel-frequency cepstral coefficients (MFCCs) and (2) a combination of the 13 MFCCs and a phonetically informed set of 20 acoustic features of vowel nasality derived from previous research. Both models are compared against two traditional approaches to estimating vowel nasalization from acoustics: A1-P0 and A1-P1, as well as their formant-compensated counterparts. Data include productions from six speakers of different language backgrounds, producing 11 different qualities within the vowel quadrilateral. The results generated from each of the methods are compared against nasometric measurements, representing an objective “ground truth” of the degree of nasalization. The results suggest that the proposed method is more robust than conventional acoustic approaches, generating signals which correlate strongly with nasometric measures across all vowel qualities and all speakers and accurately approximate the time-varying change in the degree of nasalization. Finally, an experimental example is provided to help researchers implement the method in their own study designs

An examination of oral articulation of vowel nasality in the light of the independent effects of nasalization on vowel quality

In this paper, a summary is given of an experimental technique to address a known issue in research on the independent effects of nasalization on vowel acoustics: given that the separate transfer functions associated with the oral and nasal cavities are merged in the acoustic signal, the task of teasing apart the respective effects of the two cavities seems to be an intractable problem. The results obtained from the method reveal that the independent effects of nasalization on the acoustic vowel space are: F1-raising for high vowels, F1-lowering for non-high vowels, and F2-lowering for non-front vowels. The results from previous articulatory research performed by the author on the production of vowel nasality in French, Hindi, and English are discussed in the light of these independent effects of nasalization on vowel quality

An investigation of the dynamics of vowel nasalization in Arabana using machine learning of acoustic features

This paper presents exploratory research on temporally dynamic patterns of vowel nasalization from two speakers of Arabana. To derive a dynamic measure of nasality, we use gradient tree boosting algorithms to statistically learn the mapping between acoustics and vowel nasality in a speaker-specific manner. Three primary findings emerge: (1) NVN contexts exhibit nasalization throughout the entirety of the vowel interval, and we propose that a similar co-articulatory realization previously acted to resist diachronic change in this environment; (2) anticipatory vowel nasalization is nearly as extensive as carryover vowel nasalization, which is contrary to previous claims; and (3) the degree of vowel nasalization in word-initial contexts is relatively high, even in the #_C environment, suggesting that the sound change *#Na > #a has involved the loss of the oral constriction associated with N but not the complete loss of the velum gesture

RNN Classification of English Vowels: Nasalized or Not

Vowel nasality is perceived and used by English listeners though it is not phonemic. Feature-based classifiers have been built to evaluate what features are useful for nasality perception and measurement. These classifiers require heavy high-level feature engineering with most features discrete and measured at discrete points. Recurrent neural networks can take advantage of sequential information, and has the advantage of freeing us from high-level feature engineering and potentially being stronger simulation models with a holistic view. Therefore, we constructed two types of RNN classifiers (vanilla RNN and LSTM) with MFCCs of the vowel as input to predict whether the vowel is nasalized or not. The LSTM model achieved the best performance, and supports the phonetic claim about the degree of coarticulatory nasality and the use of MFCCs for automatic speech recognition

Earbuds: A Method for Analyzing Nasality in the Field

Existing methods for collecting and analyzing nasality data are problematic for linguistic fieldworkers: aerodynamic equipment can be expensive and difficult to transport, and acoustic analyses require large amounts of optimally-recorded data. In this paper, a highly mobile and low-cost method is proposed. By connecting low impedance earbuds into a microphone jack of a recording device and placing one earbud immediately below one nostril while keeping the other earbud by the mouth, it is possible to capture the relative intensity of sound exiting the nasal and oral cavities. The two channels can then be normalized to assess the relative prominence of nasality and orality in a given speech sound. This method can not only be used to establish whether nasality is present in a speech signal, but it can also provide information about the timing and duration of nasal gestures. As such, it is an ideal tool for collecting high-quality nasality data in the field.National Foreign Language Resource Cente

A Description of the Sound System of Misiones Mbya

Misiones Mbya is an indigenous language of South America spoken by the Mbya people in the Province of Misiones, Argentina. Although there are several studies in the literature about the Brazilian variety of this language, the linguistic information available about Misiones Mbya is extremely scarce. In this thesis I present a segmental analysis of the language (individual vowels and consonants) and a prosodic analysis of nasal harmony based on field data collected in three different communities in Misiones. The segmental analysis shows that this variety is very similar to the Brazilian variety of the language with only a few exceptions. The prosodic analysis of nasal harmony indicates that nasality fades with distance. It is also shown that some methods for carrying out acoustic analysis of nasality can yield results which can be confounded with stress

Bengali nasal vowels: Lexical representation and listener perception

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Modeling of oropharyngeal articulatory adaptation to compensate for the acoustic effects of nasalization

Hypernasality is one of the most detrimental speech disturbances that lead to declines of speech intelligibility. Velopharyngeal inadequacy, which is associated with anatomic defects such as cleft palate or neuromuscular disorders that affect velopharygneal function, is the primary cause of hypernasality. A simulation study by Rong and Kuehn [J. Speech Lang. Hear. Res. 55(5), 1438–1448 (2012)] demonstrated that properly adjusted oropharyngeal articulation can reduce nasality for vowels synthesized with an articulatory model [Mermelstein, J. Acoust. Soc. Am. 53(4), 1070–1082 (1973)]. In this study, a speaker-adaptive articulatory model was developed to simulate speaker-customized oropharyngeal articulatory adaptation to compensate for the acoustic effects of nasalization on /a/, /i/, and /u/. The results demonstrated that (1) the oropharyngeal articulatory adaptation effectively counteracted the effects of nasalization on the second lowest formant frequency (F2) and partially compensated for the effects of nasalization on vowel space (e.g., shifting and constriction of vowel space) and (2) the articulatory adaptation strategies generated by the speaker-adaptive model might be more efficacious for counteracting the acoustic effects of nasalization compared to the adaptation strategies generated by the standard articulatory model in Rong and Kuehn. The findings of this study indicated the potential of using oropharyngeal articulatory adaptation as a means to correct maladaptive articulatory behaviors and to reduce nasalit

Speech Communication

Contains reports on five research projects.C.J. Lebel FellowshipNational Institutes of Health (Grant 5 T32 NSO7040)National Institutes of Health (Grant 5 R01 NS04332)National Institutes of Health (Grant 5 R01 NS21183)National Institutes of Health (Grant 5 P01 NS13126)National Institutes of Health (Grant 1 PO1-NS23734)National Science Foundation (Grant BNS 8418733)U.S. Navy - Naval Electronic Systems Command (Contract N00039-85-C-0254)U.S. Navy - Naval Electronic Systems Command (Contract N00039-85-C-0341)U.S. Navy - Naval Electronic Systems Command (Contract N00039-85-C-0290)National Institutes of Health (Grant RO1-NS21183), subcontract with Boston UniversityNational Institutes of Health (Grant 1 PO1-NS23734), subcontract with the Massachusetts Eye and Ear Infirmar