Purpose This article integrates published acoustic data on the development of vowel production. Age specific data on formant frequencies are considered in the light of information on the development of the vocal tract (VT) to create an anatomic–acoustic description of the maturation of the vowel acoustic space for English. Method Literature searches identified 14 studies reporting data on vowel formant frequencies. Data on corner vowels are summarized graphically to show age- and sex- related changes in the area and shape of the traditional vowel quadrilateral. Conclusions Vowel development is expressed as follows: (a) establishment of a language-appropriate acoustic representation (e.g., F1–F2 quadrilateral or F1–F2–F3 space), (b) gradual reduction in formant frequencies and F1–F2 area with age, (c) reduction in formant-frequency variability, (d) emergence of male–female differences in formant frequency by age 4 years with more apparent differences by 8 years, (e) jumps in formant frequency at ages corre...

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Vowel Acoustic Space Development in Children: A Synthesis of Acoustic and Anatomic Data

This paper presents the results and conclusions of a thorough study on automatic phonetic segmentation. It starts with a review of the state of the art in this field. Then, it analyzes the most frequently used approach-based on a modified Hidden Markov Model (HMM) phonetic recognizer. For this approach, a statistical correction procedure is proposed to compensate for the systematic errors produced by context-dependent HMMs, and the use of speaker adaptation techniques is considered to increase segmentation precision. Finally, this paper explores the possibility of locally refining the boundaries obtained with the former techniques. A general framework is proposed for the local refinement of boundaries, and the performance of several pattern classification approaches (fuzzy logic, neural networks and Gaussian mixture models) is compared within this framework. The resulting phonetic segmentation scheme was able to increase the performance of a baseline HMM segmentation tool from 27.12%, 79.27%, and 97.75% of automatic boundary marks with errors smaller than 5, 20, and 50 ms, respectively, to 65.86%, 96.01%, and 99.31% in speaker-dependent mode, which is a reasonably good approximation to manual segmentation.

Automatic phonetic segmentation

The growth of the vocal tract (VT) is known to be non-uniform insofar as there are regional differences in anatomic maturation. This study presents quantitative anatomic data on the growth of the oral and pharyngeal portions of the VT from 605 imaging studies for individuals between birth and 19 years. The oral (horizontal) portion of the VT was segmented into lip-thickness, anterior-cavity-length, oropharyngeal-width, and VT-oral, and the pharyngeal (vertical) portion of the VT into posterior-cavity-length, and nasopharyngeal-length. The data were analyzed to determine growth trend, growth rate, and growth type (neural or somatic). Findings indicate differences in the growth trend of segments/variables analyzed, with significant sex differences for all variables except anterior-cavity-length. While the growth trend of some variables displays prepubertal sex differences at specific age ranges, the importance of such localized differences appears to be masked by overall growth rate differences between males and females. Finally, assessment of growth curve type indicates that most VT structures follow a combined/hybrid (somatic and neural) growth curve with structures in the vertical plane having a predominantly somatic growth pattern. These data on the non-uniform growth of the vocal tract reveal anatomic differences that contribute to documented acoustic differences in prepubertal speech production.

Anatomic development of the oral and pharyngeal portions of the vocal tract: An imaging study

In this paper, we review: (1) the acoustic and linguistic properties of children's speech for both read and spontaneous speech, and (2) the developments in automatic speech recognition for children with application to spoken dialogue and multimodal dialogue system design. First, the effect of developmental changes on the absolute values and variability of acoustic correlates is presented for read speech for children ages 6 and up. Then, verbal child-machine spontaneous interaction is reviewed and results from recent studies are presented. Age trends of acoustic, linguistic and interaction parameters are discussed, such as sentence duration, filled pauses, politeness and frustration markers, and modality usage. Some differences between child-machine and human-human interaction are pointed out. The implications for acoustic modeling, linguistic modeling and spoken dialogue system design for children are presented. We conclude with a review of relevant applications of spoken dialogue technologies for children.

A review of ASR technologies for children's speech

A two-stage variable selection and classification approach for Parkinsons disease detection by using voice recording replications

Recent studies have demonstrated that analysis of laboratory-quality voice recordings can be used to accurately differentiate people diagnosed with Parkinson's disease (PD) from healthy controls (HCs). These findings could help facilitate the development of remote screening and monitoring tools for PD. In this study, 2759 telephone-quality voice recordings from 1483 PD and 15 321 recordings from 8300 HC participants were analyzed. To account for variations in phonetic backgrounds, data were acquired from seven countries. A statistical framework for analyzing voice was developed, whereby 307 dysphonia measures that quantify different properties of voice impairment, such as breathiness, roughness, monopitch, hoarse voice quality, and exaggerated vocal tremor, were computed. Feature selection algorithms were used to identify robust parsimonious feature subsets, which were used in combination with a random forests (RFs) classifier to accurately distinguish PD from HC. The best tenfold cross-validation performance was obtained using Gram-Schmidt orthogonalization and RF, leading to mean sensitivity of 64.90% (standard deviation, SD, 2.90%) and mean specificity of 67.96% (SD 2.90%). This large scale study is a step forward toward assessing the development of a reliable, cost-effective, and practical clinical decision support tool for screening the population at large for PD using telephone-quality voice.

Developing a large scale population screening tool for the assessment of Parkinson's disease using telephone-quality voice

Automatic Speech Signal Analysis for Clinical Diagnosis and Assessment of Speech Disorders provides a survey of methods designed to aid clinicians in the diagnosis and monitoring of speech disorders such as dysarthria and dyspraxia, with an emphasis on the signal processing techniques, statistical validity of the results presented in the literature, and the appropriateness of methodsthat do not requirespecialized equipment, rigorously controlled recording procedures or highly skilled personnel to interpret results. Such techniques offer the promise of a simple and cost-effective, yet objective, assessment of a range of medical conditions, which would be of great value to clinicians. The ideal scenario would begin with the collection of examples of the clients speech, either over the phone or using portable recording devices operated by non-specialist nursing staff. The recordings could then be analyzed initially to aid diagnosis of conditions, and subsequently to monitor the clients progress and response to treatment. The automation of this process would allow more frequent and regular assessments to be performed, as well as providing greater objectivity.

Automatic Speech Signal Analysis for Clinical Diagnosis and Assessment of Speech Disorders

Recent studies have demonstrated that analysis of laboratory-quality voice recordings can be used to accurately differentiate people diagnosed with Parkinson's disease (PD) from healthy controls (HC). These findings could help facilitate the development of remote screening and monitoring tools for PD. In this study, we analyzed 2759 telephone-quality voice recordings from 1483 PD and 15321 recordings from 8300 HC participants. To account for variations in phonetic backgrounds, we acquired data from seven countries. We developed a statistical framework for analyzing voice, whereby we computed 307 dysphonia measures that quantify different properties of voice impairment, such as, breathiness, roughness, monopitch, hoarse voice quality, and exaggerated vocal tremor. We used feature selection algorithms to identify robust parsimonious feature subsets, which were used in combination with a Random Forests (RF) classifier to accurately distinguish PD from HC. The best 10-fold cross-validation performance was obtained using Gram-Schmidt Orthogonalization (GSO) and RF, leading to mean sensitivity of 64.90% (standard deviation, SD 2.90%) and mean specificity of 67.96% (SD 2.90%). This large-scale study is a step forward towards assessing the development of a reliable, cost-effective and practical clinical decision support tool for screening the population at large for PD using telephone-quality voice.

Certain specific characteristics of speech are known to be particularly useful in diagnosing speech disorders by acoustic (perceptual) and instrumental methods. The most widely cited of these are described in this chapter, along with some comments as to their suitability for use in automated systems. Some of these features can be characterised by relatively simple signal processing operations, while others would ideally require a realistic model of the higher levels of neurological processing, including cognition. It is observed that even experts who come to the same ultimate decision regarding diagnosis, often differ in their assessment of individual speech characteristics. The difficulties of quantifying prosody and accurately identifying pitch epochs are highlighted, because of their importance in human perception of speech disorders.

Speech Production and Perception

Monitoring the sensory consequences of articulatory movements supports speaking. For example, delaying auditory feedback of a speaker's voice disrupts speech production. Also, there is evidence that this disruption may be decreased by immediate visual feedback, i.e., seeing one's own articulatory movements. It is, however, unknown whether delayed visual feedback affects speech production in fluent speakers. Here, the effects of delayed auditory and visual feedback on speech fluency (i.e., speech rate and errors), vocal control (i.e., intensity and pitch), and speech rhythm were investigated. Participants received delayed (by 200 ms) or immediate auditory feedback, while repeating sentences. Moreover, they received either no visual feedback, immediate visual feedback, or delayed visual feedback (by 200, 400, and 600 ms). Delayed auditory feedback affected fluency, vocal control, and rhythm. Immediate visual feedback had no effect on any of the speech measures when it was combined with delayed auditory feedback. Delayed visual feedback did, however, affect speech fluency when it was combined with delayed auditory feedback. In sum, the findings show that delayed auditory feedback disrupts fluency, vocal control, and rhythm and that delayed visual feedback can strengthen the disruptive effect of delayed auditory feedback on fluency.

Ladan Baghai-Ravary

Papers

Developing a large scale population screening tool for the assessment of Parkinson's disease using telephone-quality voice

Automatic Speech Signal Analysis for Clinical Diagnosis and Assessment of Speech Disorders

Developing a large scale population screening tool for the assessment of Parkinson's disease using telephone-quality voice

Speech Production and Perception

The effects of delayed auditory and visual feedback on speech production