Real-World Speech Recognition - Haskins Laboratories

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NSF SBE Grand Challenge White Paper: Real-World Speech Recognition Philip Rubin dynamics, and complexity. Our scientific approaches must take such issues seriously. The scientific/technological difficulties in this enterprise require both increased attention to scientific fundamentals and a multidisciplinary approach that brings together biologists, computer scientists, educators, engineers, linguistics, neuroscientists, psychologists, physicists, and social scientists. The scientific questions are numerous and difficult. Critical areas of importance include: • the physiology of speech production • neural representations of speech and language and the control of motor behavior • computational modeling of language, speech, and the mental lexicon • development and use of realistic embodied conversational agents • physical/physiological modeling of sound production • a deeper understanding of the physics of sound/gesture production • rich techniques for auditory/visual scene analysis and parsing • cognitive, emotional, cultural and social aspects of language understanding and use Attacking such difficult issues will also require investments in infrastructure and substantial advances in tool development, such as: • computational models of speech production that are open source and modular, and support comparison and addition of existing and future articulatory and aerodynamic models, include rich temporal controls to explore the evolution of physiological/linguistic events over time • multimodal, large-scale databases that provide for the display, analysis and archiving of physiological, video, audio, 4
NSF SBE Grand Challenge White Paper: Real-World Speech Recognition Philip Rubin neuroimaging, and other data, and that support automatic and expert markup and annotation • ontologies that represent linguistic, cognitive, and other knowledge • statistical/computational techniques for characterizing and analyzing temporal aspects of signals and significant events In a way, speech is more of a dance than it is a sterile enumeration of numbers, rules, or lists of features. When we talk, we usually engage in an active dialogue that spans time. How we act, how we move, and what we say are determined, in part, by the interactive, dynamically changing relationship with our partner. As with dance, our speech also conveys our personality, our heritage, our spontaneity, and our emotions. To deal with this degree of richness, computational recognition systems should embody approaches that are ecologically valid, taking into accounts the actions, goals, characteristics, and other differences of the individuals engaged in conversation. Moreover, the vagaries and realities of language use, the changing environments in which language is used, and the diverse and momentarily changing aspects of those who use it must be central concerns. Where possible, the relationship between speech and the physical system that produces it, particularly as it evolves over time, should be understood and used to constrain and simplify the recognition process (Hogden, et al., 2007; Iskarous, et al., 2010). In summary, then, the challenge is to mount a sustained, focused effort to develop recognition systems (speech, gesture, facial information, emotion, semantic, etc.) that work reliably in real-world conditions, from the workplace to the battlefield. 5
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Real-World Speech Recognition - Haskins Laboratories

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