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“Well Adjusted”: Using Robust and Flexible Speech Recognition Capabilities in Clean to Noisy Mobile Environments

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Abstract

Speech-based interfaces increasingly penetrate environments that can benefit from hands-free and/or eyes-free operations. In this chapter, a new speech-enabled framework that aims at providing a rich interactive experience for smartphone users is presented. This framework is based on a conceptualization that divides the mapping between the speech acoustical microstructure and the ­spoken implicit macrostructure into two distinct levels, namely, the signal level and ­linguistic level. At the signal level, a front-end processing that aims at improving the performance of Distributed Speech Recognition (DSR) in noisy mobile environments is performed. At this low level, the Genetic Algorithms (GAs) are used to optimize the combination of conventional Mel-Frequency Cepstral Coefficients (MFCCs) with Line Spectral Frequencies (LSFs) and formant-like (FL) features. The linguistic level involves a dialog scheme to overcome the limitations of ­current human–computer interactive applications that are mostly using constrained grammars. For this purpose, conversational intelligent agents capable of learning from their past dialog experiences are used. The Carnegie Mellon PocketSphinx engine for speech recognition and the Artificial Intelligence Markup Language (AIML) for pattern matching are used throughout­ our experiments. The evaluation results show that the inclusion of both the GA-based front-end processing and the AIML-based conversational agents leads to a significant improvement in effectiveness and performance of an interactive spoken dialog system.

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Acknowledgments

This research was funded by the Natural Sciences and Engineering Research Council of Canada and the Canada Foundation for Innovation. The author would like to thank Yacine Benahmed, Kaoukeb Kifaya, and Djamel Addou for their contributions to the development of the experimental platforms.

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Authors and Affiliations

  1. Information Management Department, Université de Moncton, Shippagan Campus, New Brunswick, Canada

    Sid-Ahmed Selouani (Professor, Chair of LARIHS (Research Lab. in Human-System Interaction)

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  1. Sid-Ahmed Selouani
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Correspondence to Sid-Ahmed Selouani .

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  1. Linguistic Technology Systems, Palisade Ave. 800, Fort Lee, 07024, New Jersey, USA

    Amy Neustein

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Selouani, SA. (2010). “Well Adjusted”: Using Robust and Flexible Speech Recognition Capabilities in Clean to Noisy Mobile Environments. In: Neustein, A. (eds) Advances in Speech Recognition. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-5951-5_5

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  • DOI: https://doi.org/10.1007/978-1-4419-5951-5_5

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  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4419-5950-8

  • Online ISBN: 978-1-4419-5951-5

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