LING620

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.


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.

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.

LIWC Software

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:

Guidi et al.,  "A wearable system for the evaluation of the human-horse interaction", Electronics 2016.

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.


Some dysfluency readings:

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 Congress of Phonetic Sciences, volume 2, pages 1513–1516. Citeseer.

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 in switchboard. In Proceedings of International Conference on Spoken Language Processing, volume 96, pages 11–14. Citeseer.

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 prosody only decision-tree model  for disfluency detection. In Fifth European Conference on Speech Communication and Technology.

 

Shriberg, E. E. (1999). Phonetic consequences of speech disfluency. Technical report, SRI INTERNATIONAL MENLO PARK CA.

 

Stolcke, A. and Shriberg, E. (1996). Statistical language modeling for speech disfluencies. In Acoustics, Speech, and Signal Processing, 1996. ICASSP-96.


Stolcke, A. and Shriberg, E. (1996). Word predictability after hesitations: a corpus-based study. ICSLP 1996.

 

Stolcke, A., Shriberg, E., Bates, R., Ostendorf, M., Hakkani, D., Plauche, M., Tur, G., and Lu, Y. (1998). Automatic detection of sentence boundaries and disfluencies based on recognized words. In Fifth InternationalConference on Spoken Language Processing.

 

Tottie, G. (2011). Uh and um as sociolinguistic markers in British English. International Journal of Corpus Linguistics, 16(2):173–197.


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.