SpeechMark Publications and Presentations

Landmark-based approach for automatically describing the effect of spasmodic dysphonia on speech production: Preliminary case studies

Keiko Ishikawaa and Joel MacAuslan

Abstract: Spasmodic dysphonia causes voice breaks in linguistically inappropriate places in speech. Landmark-based analysis automatically describes this speech segmentation error.

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Copyright (2019) This article may be downloaded for personal use only. Any other use requires prior permission of the author.


Toward clinical application of landmark-based speech analysis: Landmark expression in normal adult speech

Keiko Ishikawaa, Joel MacAuslan, Suzanne Boyce

Abstract: The goal of clinical speech analysis is to describe abnormalities in speech production that affect a speaker’s intelligibility. Landmark analysis identifies abrupt changes in a speech signal and classifies them according to their acoustic profiles. These acoustic markers, called landmarks, may help describe intelligibility deficits in disordered speech. As a first step toward clinical application of landmark analysis, the present study describes expression of landmarks in normal speech. Results of the study revealed that syllabic, glottal, and burst landmarks consist of 94% of all landmarks, and suggest the effect of gender needs to be considered for the analysis.

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Copyright (2017) Acoustical Society of America. This article may be downloaded for personal use only. Any other use requires prior permission of the author and the Acoustical Society of America.

The following article appeared in The Journal of the Acoustical Society of America 142, EL441 (2017) and may be found at http://asa.scitation.org/doi/10.1121/1.5009687.

Application of Laryngeal Landmarks for Characterization of Dysphonic Speech (2017)

Keiko Ishikawa, Joel MacAuslan, Suzanne Boyce
People don’t understand me in noisy places” is one of the most commonly reported concerns among individuals with dysphonia. Dysphonia is often a result of laryngeal pathology, which elicits greater aperiodicity and instability in a speech signal. These acoustic abnormalities likely contribute to the intelligibility deficit reported by these individuals.

Acoustic analysis is commonly used in dysphonia evaluation. Multiple algorithms are available for characterizing the degree of aperiodicity in speech. Typically, the degree of aperiodicity is measured over a particular length of voicing or speech selected by a user. While such algorithms are effective for describing degree of dysphonic voice quality perceived by listeners, an algorithm that describes timing and frequency of aperiodic moments may provide information more relevant to intelligibility.

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Predicting Intelligibility of Dysphonic Speech with Automatic Measurement of Vowel Related Parameters (2017)

Keiko Ishikawa, Meredith Meyer, Joel MacAuslan, Suzanne Boyce

• Reduced intelligibility is a common complaint among people with dysphonia.

• Vowels carry information that greatly contributes to intelligibility.

• A formant is a cluster of frequencies amplified by the vocal tract. The first two formants are critical for perception of vowels.

• A greater amount of noise and a lack of harmonic power are common characteristics of dysphonic speech signals. These acoustic abnormalities can negatively affect perceptual resolution of formants.

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Automated Analysis of Syllable Complexity as an Indicator of Speech Disorder (2017)

Marisha Speights, Joel MacAuslan, Noah Silbert, Suzanne Boyce
This study was designed to examine the feasibility of the Syllabic Cluster algorithm in the SpeechMark® MATLAB toolbox as an automated approach for identifying differences in speakers with and without Speech Sound Disorders(SSD).


  • In the course of normal development, children master voluntary coordination of the motoric movements necessary for the utterance of complex syllables.
  • Development of well-formed syllables has been shown to be a significant predictor of later communication skills.
  • Children with delayed speech production show atypical trends in the mastery of well-formed syllables, especially in continuous speech.

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Automated Screening for Speech Disorders Using Acoustic Landmark Detection (2017)

Marisha Speights, Keiko Ishikawa, Joel MacAuslan, Suzanne Boyce
The purpose of this study is to characterize differences in the speech of children with and without speech disorders by using an automated acoustic landmark based approach.
This pilot study explores entropy as a tool for characterizing differences in the landmark (LM) acoustic sequence between normal adults and children with and without a Speech Sound Disorder (SSD). Shannon’s Entropy and ROC analysis are used to evaluate the landmark patterns as potential diagnostic measures of atypical speech production. We discuss these results and our future work toward developing a fully automated clinical screening tool.

Introduction: Speech Disorders in Children

  • Speech requires precision in 1) planning and execution of articulatory targets and 2) sequencing the timing, direction, and force of the articulators. Speech is susceptible to decreased accuracy and precision due to the complexity of such movements 1,2
  • Most children effortlessly learn how to coordinate movements for normal speech production.2-5
  • About one in twelve preschool-aged children, however, show delays in speech production capability that may put them at risk for academic and behavioral difficulties, if not identified and treated.6

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Improving the Accuracy of Automatic Detection of Emotions From Speech

Reza Asadi, Harriet Fell
Computers that can recognize human emotions could react appropriately to a user’s needs and provide more human like interactions.

Some of the applications of emotion recognition:

  • Diagnostic tool for medical purposes
  • Onboard car driving systems to keep the driver alert if stress is detected[1]
  • Similar system in aircraft cockpits
  • Online tutoring

Our contributions:

  • Use new combinations of acoustic feature sets to improve the performance of emotion recognition from speech
  • Provide a comparison of feature sets for detecting different emotions

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Measurement of Child Speech Complexity Using Acoustic Landmark Detection

Keiko Ishikawa, Marepalli B. Rao, Suzanne Boyce
Dysphonia negatively affects speech intelligibility especially in the presence of background noise; however, no clinical tool exists to measure this deficit. Landmark (LM) analysis may serve as the basis of such tool.

The analysis identifies characteristic patterns of abrupt changes in the speech signal over time, and assigns them particular “landmarks.” Consequently, it describes speech as a sequence of LMs.

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Measurement of Child Speech Complexity Using Acoustic Landmark Detection

An important measure of intelligibility in young children is the ability to articulate complex syllables1-4. The development of well-formed syllables in infancy has been shown to be a significant predictor of later communication skills. 1-4 Children with delayed speech acquisition do not show this same developmental trend, and deviations in syllable acquisition may serve as a diagnostic marker of future speech delay.

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Deep Brain Stimulation May Contribute to Dysarthria in Patients with Parkinson’s Disease as Detected by Objective Measures

Craig Van Horne M.D Ph.D, Joel MacAuslan Ph.D, Karen Chenausky M.S CF-SLP, Carla Massari
Dysarthria is found in approximately 80% of patients with Parkinson’s Disease (PD) and significantly limits communication as the severity worsens. Surgical implantation of deep brain stimulators (DBS) into the subthalamic nucleus (STN) has become more common and is an effective treatment for the motoric symptoms of PD. However, the effect of DBS on speech is equivocal.

We have developed computer algorithms that quickly and objectively analyze the speech of PD patients, allowing clinicians to assess the effect of speech on DBS programming or other therapies.

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