Bibliographic record
Abstract
Speech is a product of the complex orchestration among several subsystems operating independently and collaboratively: respiration, phonation, articulation and resonance. Accordingly, studies on speech disorders often use this subsystem framework to identify and manage speech problems in an efficient, sophisticated manner. Among these subsystems, this chapter provides a brief discussion of acoustic and kinematic tools for the examination of articulation processes and disorders (see Ludlow, Kent, & Gray, 2018, for a comprehensive description of instrumental analysis across the subsystems). Articulation is typically viewed as highly coordinated gestures of the supraglottal speech organs such as the face, lips, jaw, and the tongue. In this chapter, some studies of resonance are also included in the context of discussing articulation of nasal sounds and their influence on neighboring sounds. Advantages of instrumental analysis of speech include the capability of acoustic signals to bridge the acts of speech production and speech perception and the capability of kinematic signals to bring us closer to events in the complex peripheral system that underly the shaping of acoustic signals. Further, both methods provide more objective, precise and reliable data compared to perceptual judgment. Note that kinematic studies discussed in the chapter mostly refer to electromagnetic articulography (EMA) data excluding other approaches such as brain imaging, ultrasound, and palatography (see Chapter 34), as EMA appears to be the most popular kinematic tool employed in the current literature. EMA is a method of tracking flesh point movements of the articulators (tongue, lips, jaw) that can be synchronized with acoustic signals. Currently, Carstens Medizinelektronik (Bovenden, Germany) is the only manufacturer offering a commercial EMA device, AG 501 ( https://www.articulograph.de ), since Northern Digital Inc. (NDI: Waterloo, Canada) decided to discontinue the Wave and the NDI Vox in 2020. EMA has several advantages, including its adaptability to speakers of different ages, safety for use in repeated studies (e.g., treatment efficacy), and availability of tools for data analysis. An important recent development on articulation research is a notable increase in the use of EMA. Figure 35.1 illustrates the number of articles using EMA published annually from 1987 to 2022. A PubMed search using the terms “electromagnetic articulography, EMA, speech” for the last five decades shows an accelerating pattern of EMA research. Table 35.1 includes selected recent EMA literature (within the last two decades) pertaining to the description and analysis of speech kinematics in children and adults with and without speech‐language disorders. These studies have advanced our understanding on the relationship between acoustic and kinematic phenomena (Lee et al., 2017; Mefferd, 2015; Thompson & Kim, 2019) and the effects of age on speech kinematics (van Brenk et al., 2013). Accelerating progress in speech technology and notably increasing EMA data have also ushered in a new era in clinical application. In addition to the exploration of kinematic signatures in various speech‐language disorders (Allison et al., 2022; Kopera & Grigos, 2020), studies have demonstrated the use of acoustic or kinematic signals as visual biofeedback for articulation therapy for children and adults (e.g., Haworth et al., 2019; Kearney et al., 2018; Peterson et al., 2022). The scope of the chapter is to offer a condensed and selective overview of the literature on acoustic and kinematic studies of articulation for different sound classes. We begin at the segmental level, with an examination of vowels and consonants, followed by analyses at the level of multisyllabic utterances. Each section concludes with examples of typical clinical applications. Throughout the chapter, we attempt to offer acoustic descriptions of frequent articulation problems in various populations, followed by their corresponding kinematic evidence based on a summary of progress and a blueprint for future research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.012 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".