Speech Adaptation in Extended Ambient Intelligence Environments

This Blue Sky presentation focuses on a major shift toward a notion of "ambient intelligence" that transcends general applications targeted at the general population. The focus is on highly personalized agents that accommodate individual differences and changes over time. This notion of Extended Ambient Intelligence (EAI) concerns adaptation to a person's preferences and experiences, as well as changing capabilities, most notably in an environment where conversational engagement is central. An important step in moving this research forward is the accommodation of different degrees of cognitive capability (including speech processing) that may vary over time for a given user— whether through improvement or through deterioration. We suggest that the application of divergence detection to speech patterns may enable adaptation to a speaker's increasing or decreasing level of speech impairment over time. Taking an adaptive approach toward technology development in this arena may be a first step toward empowering those with special needs so that they may live with a high quality of life. It also represents an important step toward a notion of ambient intelligence that is personalized beyond what can be achieved by mass-produced, one-size-fits-all software currently in use on mobile devices.

[1]  William J. Clancey,et al.  Ambient Personal Environment Experiment (APEX): A Cyber-Human Prosthesis for Mental, Physical and Age-Related Disabilities , 2015, AAAI Spring Symposia.

[2]  Bonnie J. Dorr,et al.  Machine Translation: A View from the Lexicon , 1994, CL.

[3]  Bonnie J. Dorr,et al.  Machine Translation Divergences: A Formal Description and Proposed Solution , 1994, CL.

[4]  Marc Schröder,et al.  The SEMAINE API: Towards a Standards-Based Framework for Building Emotion-Oriented Systems , 2010, Adv. Hum. Comput. Interact..

[5]  David J. Atkinson,et al.  Human-Machine Trust for Robust Autonomous Systems , 2012 .

[6]  James R. Glass,et al.  Lexical modeling of non-native speech for automatic speech recognition , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

[7]  Nizar Habash,et al.  Handling translation divergences: combining statistical and symbolic techniques in generation-heavy machine translation , 2002, AMTA.

[8]  Susan Fager,et al.  Communication Support for People with ALS , 2011, Neurology research international.

[9]  Sylvester Olubolu Orimaye,et al.  Learning Predictive Linguistic Features for Alzheimer’s Disease and related Dementias using Verbal Utterances , 2014, CLPsych@ACL.

[10]  Patrick Gebhard,et al.  The use of affective and attentive cues in an empathic computer-based Companions , 2010 .

[11]  Julia Hirschberg,et al.  Intoxication Detection Using Phonetic, Phonotactic and Prosodic Cues , 2011, INTERSPEECH.

[12]  R. Kraus,et al.  Air Force Office of Scientific Research , 2015 .

[13]  Anton Leuski,et al.  All Together Now - Introducing the Virtual Human Toolkit , 2013, IVA.

[14]  Nizar Habash,et al.  DUSTer: a method for unraveling cross-language divergences for statistical word-level alignment , 2002, AMTA.

[15]  Frank Rudzicz,et al.  Automatically identifying trouble-indicating speech behaviors in alzheimer's disease , 2014, ASSETS.

[16]  Susan E. Strayer,et al.  DialogueView: annotating dialogues in multiple views with abstraction† , 2008, Natural Language Engineering.

[17]  Ulrike Schultze,et al.  Embodiment and presence in virtual worlds: a review , 2010, J. Inf. Technol..

[18]  Gary L. Pattee,et al.  Bulbar and speech motor assessment in ALS: Challenges and future directions , 2013, Amyotrophic lateral sclerosis & frontotemporal degeneration.

[19]  Daniel Povey,et al.  The Kaldi Speech Recognition Toolkit , 2011 .

[20]  Yorick Wilks,et al.  CALONIS: An Artificial Companion Within a Smart Home for the Care of Cognitively Impaired Patients , 2014, ICOST.

[21]  Philip C. Woodland,et al.  Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models , 1995, Comput. Speech Lang..

[22]  J. Cassell,et al.  Embodied conversational agents , 2000 .

[23]  Roger K. Moore Computer Speech and Language , 1986 .

[24]  Mark J. F. Gales,et al.  Maximum likelihood linear transformations for HMM-based speech recognition , 1998, Comput. Speech Lang..

[25]  William J. Clancey,et al.  Shared Awareness, Autonomy and Trust in Human-Robot Teamwork , 2014, AAAI Fall Symposia.

[26]  J. Pennebaker,et al.  Language use of depressed and depression-vulnerable college students , 2004 .

[27]  Alexiei Dingli,et al.  Demonstration of a Prototype for a Conversational Companion for Reminiscing about Images , 2010, ACL.

[28]  Mathew J Page,et al.  Methods of observation in mental health inpatient units. , 2006, Nursing times.