Sequence Package Analysis and Soft Computing: Introducing a New Hybrid Method to Adjust to the Fluid and Dynamic Nature of Human Speech

  • Amy Neustein
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 87)


At Linguistic Technology Systems, we are using Sequence Package Analysis (SPA) to architect a new, pragmatically-based part of speech tagging program to better conform to the fluidity and dynamism of human speech. This would allow natural language-driven voice user interfaces and audio mining programs – for use in both commercial and government applications – to adapt to the in situ construction of dialog, marked by the imprecision, ambiguity and vagueness extant in real-world communications. While conventional part of speech (POS) tagging programs consist of parsing structures derived from syntactic (and semantic) analysis, speech system developers (and users) are also very much aware of the fact that speech recognition difficulties still plague such conventional spoken dialog systems. This is because the inherent inexactitude, vagueness, and uncertainty that are inextricable to the dynamic and fluid nature of human dialog in the real world (e.g., a sudden accretion of anger/frustration may transform a simple question into a rhetorical one; or transform an otherwise simple and straightforward assessment into a gratuitous/sardonic remark) cannot be adequately addressed by conventional POS tagging programs based on syntactic and/or semantic analysis. If we consider for a moment that the biological organism of the human mind does not appear (for the most part) to have much difficulty following the vagarious ebb and flow of dialog with remarkable accuracy and comprehension, so that business transactions and social acts are consummated with a fair amount of regularity and predictability in our quotidian lives, why can’t we design spoken dialog systems to emulate the human mind? To do this, we must first uncover the special formulae that humans regularly invoke to understand humanto- human dialog which by virtue of its fluid and dynamic constitution is often punctuated by ambiguities, obscurities, repetitions, ellipses, and deixes (indirect referents) – the same stubborn and ineluctable features of natural language which individually and collectively impede the performance of speech systems. Using a unique set of parsing structures – consisting of context-free grammatical units, with notations for related prosodic features – to capture the fluid/dynamic nature of human speech, SPA meets the goal of soft computing to exploit the tolerance for imprecision, uncertainty, obscurity, and approximation in order to achieve tractability, robustness and low solution cost. And as a hybrid method – uniquely combining conversation analysis with computational linguistics – SPA is complementary to artificial neural networks and fuzzy logic because in building a flexible and adaptable natural language speech interface, neural networks, or connectionist models, may be viewed as the natural choice for investigating the patterns underlying the orderliness of talk, as they are equipped to handle the ambiguities of natural language due to their capacity, when confronted with incomplete or somewhat conflicting information, to produce a fuzzy set.


Sequence Package Analysis Part-of-Speech Tagging Artificial Neural Networks Fuzzy Logic Conversation Analysis Natural Language Understanding Soft Computing Voice-User Interface Audio Mining 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Neustein, A.: Using Sequence Package Analysis to Improve Natural Language Understanding. International Journal of Speech Technology 4(1), 31–44 (2001)zbMATHCrossRefGoogle Scholar
  2. 2.
    Neustein, A.: Sequence Package Analysis: A New Natural Language Understanding Method for Improving Human Response in Critical Systems. International Journal of Speech Technology 9(3-4), 109–120 (2008)CrossRefGoogle Scholar
  3. 3.
    Button, G., Coulter, J., Lee, J.R.E., Sharrock, W.: Computers, Minds and Conduct. Polity Press, Cambridge (1995)Google Scholar
  4. 4.
    Button, G.: Going Up a Blind Alley: Conflating Conversation Analysis and Computational Modeling. In: Luff, P., Gilbert, N., Frolich, D.M. (eds.) Computers and Conversation, pp. 67–90. Academic Press, London (1990)Google Scholar
  5. 5.
    Button, G., Sharrock, W.: On Simulacrums of Conversation: Toward a Clarification of the Relevance of Conversation Analysis for Human-Computer Interaction. In: Thomas, P.J. (ed.) The Social and Interactional Dimensions of Human-Computer Interfaces, pp. 107–125. Cambridge University Press, Cambridge (1995)Google Scholar
  6. 6.
    Schegloff, E.A.: To Searle on Conversation: A Note in Return. In: Verschueven, J. (ed.) Searle on Conversation. Pragmatics and Beyond New Series, vol. 21, pp. 113–128. John Benjamins Publishing Co., Amersterdam (1992)Google Scholar
  7. 7.
    Gilbert, G.N., Wooffitt, R.C., Frazer, N.: Organizing Computer Talk. In: Luff, P., Gilbert, N., Frohlich, D.M. (eds.) Computers and Conversation, pp. 235–257. Academic Press, London (1990)Google Scholar
  8. 8.
    Hirst, G.: Does Conversation Analysis Have A Role in Computational Linguistics? Computational Linguistics 17(2), 211–227 (1991)Google Scholar
  9. 9.
    Hutchby, I., Wooffitt, R.: Conversation Analysis: Principles, Practices and Applications. Polity Press, Cambridge (1998)Google Scholar
  10. 10.
    Frankel, R.: Talking in Interviews: A Dispreference for Patient-Initiated Questions in Physician-Patient Encounters. In: Psathas, G. (ed.) Interaction Competence, pp. 231–262. University Press of America, Washington, D.C (1990)Google Scholar
  11. 11.
    Neustein, A.: Sequence Package Analysis: A New Global Standard for Processing Natural Language Input? Globalization Insider, XIII(1,2) (2004)Google Scholar
  12. 12.
    Button, G.: Moving out of Closings. In: Button, G., Lee, J.R.E. (eds.) Talk and Social Organization, pp. 101–151. Multilingual Matters, Clevedon (1987)Google Scholar
  13. 13.
    Sacks, H.: posthumous publication of Harvey Sack’s lecture notes. In: Jefferson, G. (ed.) Lectures on Conversation, vol. 11, p. ix-580. Blackwell, Oxford (1992)Google Scholar
  14. 14.
    Jefferson, G., Lee, J.R.E.: The Rejection of Advice: Managing the Problematic Convergence of Troubles-Telling and a Service Encounter. Journal of Pragmatics 5, 399–422 (1981)CrossRefGoogle Scholar
  15. 15.
    Neustein, A.: Sequence Package Analysis: A New Method for Intelligent Mining of Patient Dialog, Blogs and Help-line Calls. Journal of Computers 2(10), 45–51 (2007)CrossRefGoogle Scholar
  16. 16.
    Emmison, M.: Calling for Help, Charging for Support: Some Features of the Introduction of Payment as a Topic in Calls to a Software Help-Line. In: Symposium on Help-Lines, Aalborg, Denmark, September 8-10 (2000)Google Scholar
  17. 17.
    Lee, C.M., Narayanan, S.S.: Toward Detecting Emotions in Spoken Dialogs. IEEE Transactions on Speech and Audio Processing 13(2), 293–303 (2005)CrossRefGoogle Scholar
  18. 18.
    Schmitt, A., Pieraccini, R., Polzehl, T.: For Heaven’s Sake, Gimme a Live Person! Designing Emotion-Detection Customer Care Voice Applications in Automated Call Centers. In: Neustein, A. (ed.) Advances in Speech Recognition: Mobile Environments, Call Centers and Clinics, pp. 191–219. Springer, Heidelberg (2010)Google Scholar
  19. 19.
    Heritage, J.C., Watson, D.R.: Formulating as Conversational Objects. In: Psathas, G. (ed.) Everyday Language: Studies in Ethnomethodology, pp. 123–162. Irvington Publishers, New York (1979)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Amy Neustein
    • 1
  1. 1.Linguistic Technology SystemsFort LeeUSA

Personalised recommendations