Spring 2018
Research Topics in Clinical Linguistics
This syllabus will adapt to reflect the background and interests of class participants.
TOPIC 1: We're going to start by looking at the linguistic correlates of various neurodegenerative disorders. For background, take a look at the Penn Frontotemporal Degeneration Center's pages "About FTD and Related Disorders".
Snowdon et al., "Linguistic ability in early life and cognitive function and Alzheimer's disease in late life", JAMA 1996.
Snowdon et al., "Linguistic ability in early life and the neuropathology of Alzheimer's disease and cerebrovascular disease", Ann N Y Acad Sci 2000.
Elias et al., "The preclinical phase of Alzheimer disease: a 22-year prospective study of the Framingham Cohort", Archives of Neurology 2000.
DeCarli et al., "Measures of brain morphology and infarction in the Framingham Heart Study: establishing what is normal", Neurobiology of Aging 2005.
Au et al., "Association
of white matter hyperintensity volume with decreased cognitive
functioning: the Framingham Heart Study", Archives of
Neurology 2006
Brockmann et al., "Voice
loudness and gender effects on jitter and shimmer in healthy adults",
J. of Speech Language and Hearing Research 2008.
Silva
et al., "Jitter
Estimation Algorithms for Detecton of Pathological Voices",
EURASIP Signal Processing 2009.
Le et al., "Longitudinal detection of dementia through lexical and syntactic changes in writing: a case study of three British novelists", Literary and Linguistic Computing 2011.
Brockmann et al., "Reliable jitter and shimmer measurements in voice clinics: the relevance of vowel, gender, vocal intensity, and fundamental frequency effects in a typical clinical task", Journal of Voice 2011.
Hirst & Wei Feng, "Changes
in style in authors with Alzheimer's disease", English
Studies 2012.
Fraser et al., "Automated classification of primary progressive aphasia subtypes from narrative speech transcripts", Cortex 2012.
Ash et al., "Differentiating primary progressive aphasias in a brief sample of connected speech", Neurology 2013.
Forbes-McKay et al., "Profiling spontaneous speech decline in Alzheimer's disease: a longitudinal study", Acta Neuropsychiatrica 2013.
Tsantali et al., "Could language deficits really differentiate Mild Cognitive Impairment (MCI) from mild Alzheimer's disease?", Archives of Gerontology and Geriatrics 2013.
Fraser et al., "Using text and acoustic features to diagnose progressive aphasia and its subtypes", Interspeech 2013.
Ash et al., "Narrative discourse deficits in amyotrophic lateral sclerosis", Neurology 2014.
Bondi et al., "Neuropsychological criteria for mild cognitive impairment improves diagnostic precision, biomarker associations, and progression rates", Journal of Alzheimer's Disease 2014.
Fraser et al., "Using statistical parsing to detect agrammatic aphasia", BioNLP 2014.
Fraser et al., "Comparison of different feature sets for identification of variants in progressive aphasia", Workshop on Computational Linguistics and Clinical Psychology 2014.
Meilán et al., "Speech in Alzheimer's Disease: Can Temporal and Acoustic Parameters Discriminate Dementia?", Dementia and Geriatric Cognitive Disorders, 2014.
Ash & Grossman, "Why study connected speech?", in R. M. Willems (Ed.), Cognitive Neuroscience of Natural Language Use, 2015.
Au et al., "Gender and incidence of dementia in the Framingham Heart Study from mid-adult life", Alzheimer's & Dementia 2015.
Berisha et al., “Tracking Discourse Complexity Preceding Alzheimer's Disease Diagnosis: A Case Study Comparing the Press Conferences of Presidents Ronald Reagan and George Herbert Walker Bush”, Journal of Alzheimer’s Disease 2015
Dodge et al. "Social markers of mild cognitive impairment: Proportion of word counts in free conversational speech", Current Alzheimer Research, 2015
Fraser et al., "Sentence segmentation of aphasic speech", NAACL 2015.
Karmele López-de-Ipina et al., "Feature selection for spontaneous speech analysis to aid in Alzheimer's disease diagnosis: A fractal dimension approach", Computer Speech and Language 2015.
König et al., "Automatic speech analysis for the assessment of patients with predementia and Alzheimer's disease", Alzheimer's & Dementia 2015.
Yancheva et al., "Using text and acoustic features to diagnose progressive aphasia and its subtypes", SLPAT 2015.
Fraser et al., "Detecting late-life depression in Alzheimer's disease through analysis of speech and language", HLT-NAACL 2016.
Fraser & Hirst, "Detecting semantic changes in Alzheimer’s disease with vector space models", LREC 2016.
Hirst et al., "Method and system of longitudinal detection of dementia through lexical and syntactic changes in writing", U.S. Patent 951428182, 2016.
Jun et al., "A novel Alzheimer disease locus located near the gene encoding tau protein" ,Molecular Psychiatry 2016.
An et al., "Data Platform for the Research and Prevention of Alzheimer’s Disease", Healthcare and Big Data Management 2017.
Asgari et al., "Predicting mild cognitive impairment from spontaneous spoken utterances", Alzheimer's and Dementia 2017.
Au et al., "How technology is reshaping cognitive assessment: Lessons from the Framingham Heart Study", Neuropsychology 2017.
Nevler et al., "Automatic Measurement of Prosody in Behavioral Variant FTD", Neurology 2017
Sirts et al., "Idea density for predicting Alzheimer's disease from transcribed speech", 2017.
Mirheidari et al., "An avatar-based system for identifying individuals likely to develop dementia", Interspeech 2017.
Boschi et al., "Connected Speech in Neurodegenerative Language Disorders: A Review", Frontiers of Psychology 2017.
De Belder et al., "Impaired Processing of Serial Order Determines Working Memory Impairments in Alzheimer’s Disease", Journal of Alzheimer's Disease 2017.
Gitit
Kavé & Mira Goral, "Word retrieval in
connected speech in Alzheimer's disease: a review with
meta-analyses", Aphasiology 2018
Gitit
Kavé & Ayelet Dassa, "Severity of
Alzheimer's disease and language features in picture descriptions",
Aphasiology 2018
Cera
et al., "Phonetic and phonological aspects of
speech in Alzheimer's disease", Aphasiology 2018
Abdall et al., "Rhetorical structure and Alzheimer's disease", Aphasiology 2018.
Emrani
et al., "Assessing
Working Memory in Mild Cognitive Impairment with Serial Order
Recall", Journal of Alzheimer's Disease 2018.
Eyigoz
et al., "Unsupervised
Morphological Segmentation for Detecting Parkinson’s Disease",
AAAI 2018.
Eyigoz
et al., "Predicting
Cognitive Impairments with a Mobile Application", IBM ms.
2018
Hernández-Domínguez et al., "Computer-based evaluation of Alzheimer’s disease and mild cognitive impairment patients during a picture description task", Alzheimer's & Dementia 2018
Thomas
et al., "Word-list
intrusion errors predict progression to mild cognitive impairment",
Neuropsychology 2018.
Toledo
et al., "Analysis
of macrolinguistic aspects of narratives from individuals with
Alzheimer's disease, mild cognitive impairment, and no cognitive
impairment", Alzheimer's & Dementia 2018.
Huang et al., "Adolescent Cognitive Aptitudes and Later-in-Life Alzheimer Disease and Related Disorders", JAMA 2018.
Software:
Computer Analysis of Speech for Psychological Research
Idea Density from Dependency Trees
See also: "Writing
Style and Dementia", 12/3/2004
Key questions for each paper: What exactly did they do? What were the
results? What are the potential problems?
Overall questions: What are the features/measurements/metrics that are
found or claimed to be diagnostically relevant?
You might also read through the introductory materials for the LDC's collaborative project with the Framingham Heart Study.
We now also have access to the Dementia Bank datasets -- These will be set up on harris.sas.upenn.edu in a group-limited directory. The Pitt Corpus looks especially interesting, and the Hopkins Corpus as well.
TOPIC 2: Schizophrenia
For some general background, see the NIMH's Schizophrenia page, the American Psychiatric Association's page, etc.
Cohen et al., "Referent communication disturbances in acute schizophrenia", J. of Abnormal Psych. 1974.
Rosenberg et al., "Verbal behavior and schizophrenia", Arch. Gen. Psychiatry 1979.
Solovay et al., "Scoring manual for the Thought Disorder Index", Schizophrenia Bulletin 1986.
Oxman et al., "The language of altered states", J. of Nervous and Mental Disease 1988.
Covington, "Schizophrenia and the structure of language: the linguist's view", Schizophrenia Research 2005.
Elvevåg et al., "Quantifying incoherence in speech: An automated methodology and novel application to schizophrenia", Schizophrenia Research 2007.
Junghaenel et al., "Linguistic Dimensions of Psychopathology: A Quantitative Analysis", Journal of Social and Clinical Psychology 2008.
Rish et al., "Discriminative Network Models of Schizophrenia", NIPS 2009.
Strous et al., "Automated characterization and identification of schizophrenia in writing", J. Nervous and Mental Disease 2009.
Elvevåg et al., "An automated method to analyze language use in patients with schizophrenia and their first degree relatives", J. of Neurolinguistics 2010.
Kuperberg, "Language in schizophrenia part 1: an introduction", Language and linguistics compass 2010.
Covington et al., "Phonetic measures of reduced tongue movement correlate with negative symptom severity in hospitalized patients with first-episode schizophrenia-spectrum disorders", Schizophrenia Research 2012.
Hong et al., "Lexical Differences in Autobiographical Narratives from Schizophrenic Patients and Healthy Controls", EMNLP-CoNLL 2012.
Howes et al., "Predicting adherence to treatment for schizophrenia from dialogue transcripts", ACL Sig on Discourse and Dialogue, 2012.
Mota et al., " Speech
graphs provide a quantitative measure of thought disorder in
psychosis", PloS one 2012.
Howes
et al., "Using
conversation Topics for predicting Therapy Outcomes in
Schizophrenia", Biomedical Information Insights 6,
2013.
Bedi et al., "Automated analysis of free speech predicts psychosis onset in high-risk youths", Schizophrenia 2015.
Brown and Kuperberg, "A hierarchical generative framework of language processing: Linking language perception, interpretation, and production abnormalities in schizophrenia", Frontiers in Human Neuroscience 2015.
Hinzen & Rosselló, "The linguistics of schizophrenia: thought disturbance as language pathology across positive symptoms", Frontiers in Psychology 2015.
Hong et al., "Lexical Use in Emotional Autobiographical Narratives with Schizophrenia and Healthy Controls", Psychiatry Research 2015
Mitchell et al. "Quantifying the Language of Schizophrenia in Social Media", CL-Psych 2015.
Li and Jurafsky, "Neural Net Models of Open-Domain Discourse Coherence", EMNLP 2017.
Corcoran et al., "Prediction of psychosis across protocols and risk cohorts using automated language analysis", World Psychiatry 2018.
Iter et al., "Automatic
Detection of Incoherent Speech for Diagnosing Schizophrenia",
CLPsych 2018.
Moore et al., "Development and Public Release of a Computerized Adaptive (CAT) Version of the Schizotypal Personality Questionnaire", Psychiatry Research 2018.
Nicodemus et al., "Category fluency, latent semantic analysis and schizophrenia: a candidate gene approach", Cortex 2018.
Holshausen et al., "Latent semantic variables are associated with formal thought disorder and adaptive behavior in older inpatients with schizophrenia", Cortex 2018.
Some non-clinical research on text coherence metrics:
Graesser et al., "Coh-Metrix: Analysis of text on cohesion and language", Behavior Research Methods 2004.
Mitsakaki and Kukich, "Evaluation of text coherence for electronic essay scoring systems", Natural Language Engineering 2004.
Lapata
and Barzilay. "Automatic
evaluation of text coherence: Models and representations", IJCAI
2005.
Barzilay
and Lapata, "Modeling
local coherence: An entity-based approach", CL 2008.
Burstein
et al., "Using
entity-based features to model coherence in student essays",
ACL 2010.
Crossley
and McNamara,"Text
coherence and judgments of essay quality: Models of quality and
coherence" , Cognitive
Science Society
2011.
Eisner
and Charniak, "Extending
the entity grid with entity-specific features", ACL 2011.
Feng
and Hirst, "Extending
the entity-based coherence model with multiple ranks", ACL
2012.
Louis
and Nenkova, "A
coherence model based on syntactic patterns", EMNLP 2012.
Röder et al., "Exploring the space of topic coherence measures", ACM 2015.
Crossley et al., "The tool for the automatic analysis of text cohesion (TAACO): Automatic assessment of local, global, and text cohesion", Behavior research methods 2016.
Li
and Jurafsky, "Neural
Net Models of Open-Domain Discourse Coherence", EMNLP 2017.
Syed and Spruit, "Full-text or abstract? Examining topic coherence scores using latent dirichlet allocation", IEEE ICDSAA 2017.
Also:
Farbood
et al., "The
neural processing of hierarchical structure in music and speech at
different timescales", Frontiers in Neuroscience 2015.
Rubin et al., "Participant, rater, and computer measures of coherence in posttraumatic stress disorder." Journal of abnormal psychology 2016.
TOPIC
3: Emotion/Mood/Attitude/Personality
A
few background papers on speech, emotion/mood/attitude, and
personality:
Scherer,
"Personality
inference from voice quality: The loud voice of extroversion",
European
Journal of Social Psychology
1978.
Ellgring
et al., "Vocal indicators
of mood change in depression", Journal
of Nonverbal Behavior
1996.
Gobl
and Nı Chasaide, "The
role of voice quality in communicating emotion, mood and attitude",
Speech
communication
2003.
Cowie
and Cornelius, "Describing
the emotional states that are expressed in speech", Speech
communication
2003.
Russell
et al., "Facial
and vocal expressions of emotion", Annual
review of psychology
2003.
Laukka et al., "Expression of affect in spontaneous speech: Acoustic correlates and automatic detection of irritation and resignation", Computer Speech & Language 2011.
El
Ayadi et al., "Survey
on speech emotion recognition: Features, classification schemes,
and databases", Pattern
Recognition
2011.
Schuller,
"Voice
and Speech Analysis: In Search of States and Traits", in Salah
and Gevers, Eds., Computer Analysis of Human Behavior, 2011.
Scherer, "Vocal markers of emotion: Comparing induction and acting elicitation", Computer Speech & Language 2013.
Bänziger et al., "The role of perceived voice and speech characteristics in vocal emotion communication", Journal of nonverbal behavior 2014.
Vinciarelli et al., "A survey of personality computing", IEEE Transactions on Affective Computing 2014
Bänziger et al, "Path models of vocal emotion communication", PloS one 2015.
Lombardi
and Guccione, "Analysis
of Emotions in Vowels: a Recurrence Approach", SIGNAL
2016.
[Inventory
of RQA software]
Lombardi
et al., "Exploring
Recurrence Properties of Vowels for Analysis of Emotions in Speech",
Sensors & Transducers 2016.
Guidi
et al., "Features
of vocal frequency contour and speech rhythm in bipolar disorder",
Biomedical
Signal Processing and Control
2017.
Or et al., "High potential but limited evidence: Using voice data from smartphones to monitor and diagnose mood disorders", Psychiatric rehabilitation journal 2017.
Hashim et al., "Evaluation of voice acoustics as predictors of clinical depression scores", Journal of Voice 2017.
Some
papers on mood analysis, mostly text-based:
Pennebaker
and King, "Linguistic
styles: Language use as an individual difference", Journal
of Personality and Social Psychology 1999.
Pennebaker
et al., "Psychological
aspects of natural language use: Our words, our selves", Annual
Review of Psychology 2003.
Tasczik and Pennebaker, "The psychological meaning of words: LIWC and computerized text analysis methods", Journal of Language and Social Psychology 2010.
Gratch
et al., "The
Distress Analysis Interview Corpus of human and computer
interviews", LREC 2014.
Pennebaker
et al., "The
development and psychometric properties of LIWC2015", 2015.
Fineberg
et al., "Self-reference
in psychosis and depression: a language marker of illness", Psychological
Medicine 2016.
Jamil et al., "Monitoring Tweets for Depression to Detect At-risk Users", CL-Psych 2017.
Kshirsagar
et al., "Detecting
and Explaining Crisis", CL-Psych 2017.
Morales
et al., "A
Cross-modal Review of Indicators for Depression Detection Systems",
CL-Psych 2017.
Nguyen et al., "Using linguistic and topic analysis to classify sub-groups of online depression communities", Multimedia tools and applications 2017.
Newell
et al., "You
Sound So Down: Capturing Depressed Affect Through Depressed
Language", Journal
of Language and Social Psychology
2017.
Shen
and Rudzicz, "Detecting
Anxiety through Reddit", CL-Psych 2017.
Slides
from Klaus Scherer's InterSpeech 2015 keynote: "Voices
of power, passion, and personality"
The
Computational
Paralinguistics Challenge (ComParE) 2009-20017
The Interspeech
2018 Computational Paralinguistics Challenge
And just for fun:
Lanata et al., "The role of nonlinear coupling in Human-Horse Interaction: A preliminary study", IEEE EMBC 2017
A few interesting papers on neurocognitive aspects of mood disorders:
Ellgring and Scherer, "Vocal indicators of mood change in depression", Journal of Nonverbal Behavior 1996.
Jensen et al., "Discrete neurocognitive subgroups in fully or partially remitted bipolar disorder: Associations with functional abilities", Journal of Affective Disorders 2016.
Correa-Ghisays et al., "Manual motor speed dysfunction as a neurocognitive endophenotype in euthymic bipolar disorder patients and their healthy relatives. Evidence from a 5-year follow-up study", Journal of Affective Disorders 2017.
Merikangas et al., "Neurocognitive performance as an endophenotype for mood disorder subgroups", Journal of Affective Disorders 2017.
Bora, "Neurocognitive
features in clinical subgroups of bipolar disorder: A meta-analysis",
Journal of Affective Disorders 2018.
TOPIC 4: Autism
Stewart
and Ota, "Lexical
effects on speech perception in individuals with 'autistic'
traits", Cognition 2008
Yu, "Perceptual Compensation Is Correlated with Individuals' 'Autistic' Traits: Implications for Models of Sound Change", PloS one 2010
Bishop, "Focus, prosody, and individual differences in 'autistic' traits: Evidence from cross-modal semantic priming", UCLA Working Papers 2012
Gernsbacher
et al., "Language
and Speech in Autism", Annual Review of Linguistics
2015
Kang et al., "Individual differences in autistic traits and variability in production patterns: a case of affricates by young Seoul Korean speakers", J. Korean Soc. of Speech Sciences 2015
Parish-Morris
et al., "Exploring
Autism Spectrum Disorders Using HLT", NAACL 2016
Parish-Morris
et al., "Linguistic
Camouflage in girls with autism spectrum disorder", Molecular
Autism 2017.
Beselmeyer et al., "Adaptation to Vocal Expressions and Phonemes Is Intact in Autism Spectrum Disorder", Clinical Psychological Science 2018.
Some relevant general papers:
Sidtis
et al., "Dysprosodic
speech following basal ganglia insult: Toward a conceptual
framework for the study of the cerebral representation of
prosody", Brain and Language 2006.
Skodda
et al., "Intonation
and Speech Rate in Parkinson's Disease: General and Dynamic
Aspects and Responsiveness to Levodopa Admission", Journal
of Voice 2011.
Ross
et al., "Prosodic
stress: Acoustic, aphasic, aprosodic and neuroanatomic
interactions", Journal of Neurolinguistics 2013.
Adell,
J., Escudero, D., and Bonafonte,
A. (2012). Production
of filled pauses in concatenative
speech synthesis based on the underlying fluent sentence. Speech Communication,
54(3):459–476.
Ahmed, S., Haigh, A.-M. F., de Jager, C. A., and Garrard, P. (2013). Connected speech as
a marker of disease progression in autopsy-proven Alzheimers disease. Brain, 136(12):3727–
3737.
Arciuli,
J., Mallard, D., and Villar,
G. (2010). Um, I can
tell you’re lying: Linguistic markers of deception versus
truth-telling in speech. Applied Psycholinguistics,
31(3):397– 411.
Arnold,
J. E., Kam, C. L. H., and Tanenhaus, M.
K. (2007). If you say thee
uh you are
describing something hard: The on-line attribution of disfluency
during reference comprehension. Journal of
Experimental Psychology: Learning, Memory,
and Cognition, 33(5):914.
Beattie,
G. W. and Butterworth, B. L. (1979). Contextual
probability and word frequency as determinants of pauses and
errors in spontaneous speech. Language and speech, 22(3):201–211.
Bell,
A., Jurafsky, D., Fosler-Lussier,
E., Girand, C., Gregory, M.,
and Gildea, D. (2003). Effects
of disfluencies, predictability, and utterance position on word
form variation in English conversation. The Journal ofthe Acoustical Society of America, 113(2):1001–1024.
Bortfeld,
H., Leon, S. D., Bloom, J. E., Schober, M. F., and Brennan, S. E.
(2001). Disfluency
rates in conversation: Effects of age, relationship, topic,
role, and gender. Language and speech, 44(2):123–147.
Brennan,
S. E. and Williams, M. (1995). The
feeling of another's knowing: Prosody and filled pauses as cues
to listeners about the metacognitive states of speakers. Journal
of memory and language, 34(3):383–398.
Colman,
M. and Healey, P. (2011). The
distribution of repair in dialogue. In Proceedings of the Annual Meeting of the Cognitive Science Society, volume 33.
Corley, M.
and Stewart, O. W. (2008). Hesitation
disfluencies in spontaneous speech: The meaning of um. Language
and Linguistics Compass, 2(4):589–602.
Engelhardt, P. E., Corley, M., Nigg,
J. T., and Ferreira, F.
(2010). The
role of inhibition in the production of disfluencies. Memory
& Cognition, 38(5):617–628.
Ferreira, F.
and Bailey, K.
G. (2004). Disfluencies and human language comprehension. Trends
in cognitive sciences, 8(5):231–237.
Fraundorf,
S. H. and Watson, D.
G. (2011). The disfluent discourse: Effects
of filled pauses on recall. Journal of memory and language,
65(2):161–175.
Goldwater,
S., Jurafsky, D.,
and Manning, C. D. (2010). Which words are hard to recognize?
Prosodic, lexical, and disfluency factors that increase speech
recognition error rates. Speech Communication, 52(3):181–200.
Hough, J. (2014). Modelling Incremental Self-Repair Processing in Dialogue. PhD thesis, Queen Mary University of London.
Lai, C., Gorman, K., Yuan, J., & Liberman, M. (2007). Perception of disfluency: language differences and listener bias. In Eighth Annual Conference of the International Speech Communication Association.
Lake,
J. K., Humphreys, K. R., and Cardy, S.
(2011). Listener
vs. speaker-oriented aspects of speech: Studying the disfluencies of individuals with autism spectrum disorders. Psychonomic
bulletin & review, 18(1):135–140.
Lease,
M., Johnson, M., and Charniak,
E. (2006). Recognizing
disfluencies in conversational speech. IEEE Transactions on Audio, Speech, and Language Processing, 14(5):1566–
1573.
Liu,
Y., Shriberg, E., Stolcke,
A., Hillard, D., Ostendorf,
M., and Harper, M. (2006). Enriching
speech recognition with automatic detection of sentence
boundaries and disfluencies. IEEE Transactions on audio, speech,and language processing, 14(5):1526–1540.
MacGregor,
L. J., Corley, M.,
and Donaldson, D. I. (2009). Not all disfluencies are are equal: The effects of disfluent repetitions on language comprehension. Brain and language,
111(1):36–45.
McDaniel,
D., McKee, C., and Garrett, M. F. (2010). Children’s
sentence planning: Syntactic correlates of fluency variations. Journal
of Child Language, 37(1):59–94.
Moniz,
H., Batista, F., Mata, A. I., and Trancoso, I.
(2014). Speaking
style effects in the production
of disfluencies. Speech Communication, 65:20–35.
Nakatani,
C. H. and Hirschberg, J. (1994). A
corpus-based study of repair cues in spontaneous speech. The
Journal of the Acoustical Society
of America, 95(3):1603–1616.
Ostendorf,
M. and Hahn, S. (2013). A
sequential repetition model for improved disfluency detection.
In INTERSPEECH, pages 2624–2628.
Pakhomov, S. and Savova, G. (1999). Filled pause distribution and modeling in quasi-spontaneous speech. In Proceedings of the International Conference of Phonetic Sciences.
Parish-Morris, J., Liberman, M. Y., Cieri, C., Herrington, J. D., Yerys, B. E., Bateman, L., ... & Schultz, R. T. (2017). Linguistic camouflage in girls with autism spectrum disorder. Molecular autism, 8(1), 48.
Plauch´e, M. and Shriberg, E. (1999). Data-driven subclassification of disfluent repetitions based
on prosodic features. In Proc. International
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Rohrer, J. D., Knight, W. D., Warren, J. E., Fox, N. C., Rossor, M. N., and Warren, J. D. (2008). Word-finding difficulty: a clinical analysis of the progressive aphasias. Brain, 131(1):8–38.
Seifert
et al. (2018). Nouns
slow down speech: evidence from structurally and culturally
diverse languages. PNAS.
Schachter, S., Christenfeld,
N., Ravina, B., and Bilous,
F. (1991). Speech
disfluency and the structure of knowledge. Journal
of Personality and Social Psychology, 60(3):362.
Shriberg,
E. (1996). Disfluencies
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Shriberg, E. (2001). To errrris human: ecology and acoustics of speech disfluencies. Journal
of the International Phonetic Association,
31(1):153–169.
Shriberg,
E., Bates, R., and Stolcke,
A. (1997). A
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Shriberg,
E. E. (1999). Phonetic
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Stolcke,
A. and Shriberg, E. (1996). Word
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1996.
Stolcke,
A., Shriberg, E., Bates,
R., Ostendorf, M., Hakkani,
D., Plauche, M., Tur, G.,
and Lu, Y. (1998). Automatic
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Wang, W., Stolcke, A., Yuan, J., & Liberman, M. (2013). A cross-language study on automatic speech disfluency detection. In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 703-708).
Wieling, M., Grieve, J., Bouma, G., Fruehwald, J., Coleman, J., & Liberman, M. (2016). Variation and change in the use of hesitation markers in Germanic languages. Language Dynamics and Change, 6(2), 199-234.
Yuan, J., Xu, X., Lai, W., & Liberman, M. (2016). Pauses and pause fillers in Mandarin monologue speech: The effects of sex and proficiency. Proceedings of Speech Prosody 2016, 1167-1170.