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Design and portable device implementation of feature-based partial matching algorithms for personal spoken sentence retrieval

Design and portable device implementation of feature-based partial matching algorithms for personal spoken sentence retrieval

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With the increasingly widespread use of personal portable devices, it is essential to devise an efficient method for spoken data retrieval for its resource-limited identity. This investigation proposes two efficient feature-based sentence-matching algorithms for speaker-dependent personal spoken sentence retrieval. Such a system can efficiently retrieve database sentences only partially matched to query sentence inputs. The query and database sentences are initially segmented into equal-sized matching units. A matching plane that comprises matching blocks is then created. For each matching block, a local similarity score is then determined from the feature distance. A whole-matching-plane-based accumulation scheme and a column-based row-based accumulation scheme are then designed to determine the global similarity score. The global similarity score of the matching plane reveals the similarity between the query and database sentences. The proposed algorithms are based on the feature-level comparison and do not require acoustical and language models. Experiments on news titles and personal schedules were conducted. The experimental results show that the proposed algorithms can efficiently work on both PC and HP iPAQ H5550 PDA.

References

    1. 1)
    2. 2)
      • Ng, K., Zue, V.: `Phonetic recognition for spoken document retrieval', Proc. Int. Conf. Acoustics, Speech, Signal Processing, 1998, p. 325–328.
    3. 3)
    4. 4)
      • Meng, H.M., Hui, P.Y.: `Spoken document retrieval for the languages of Hong Kong', Proc. 2001 Int. Symp. Intelligent Multimedia, Video and Speech Processing, 2001, p. 201–204.
    5. 5)
      • Johnson, S.E., Jourlin, P., Moore, G.L., Spärck Jones, K., Woodland, P.C.: `The Cambridge University spoken document retrieval system', Proc. Int. Conf. Acoustics, Speech, Signal Processing, 1999, p. 49–52.
    6. 6)
      • Wechsler, M.: `Spoken document retrieval based on phoneme recognition', 1998, PhD, Swiss Federal Institute of Technology (ETH), Zurich.
    7. 7)
      • Srinivasan, S., Petkovic, D.: `Phonetic confusion matrix based spoken document retrieval', Proc. Int. Annual ACM SIGIR Conf. Research and Development in Information Retrieval Proceedings, 2000, p. 81–87.
    8. 8)
      • Singhal, A., Pereira, F.: `Document expansion for speech retrieval', Proc. Int. Annual ACM SIGIR Conf. Research and Development in Information Retrieval, 1999, p. 34–41.
    9. 9)
      • Crestani, F.: `Towards the use of prosodic information for spoken document m retrieval', Proc. Int. Annual ACM SIGIR Conf. Research and Development in Information Retrieval, 2001, p. 420–421.
    10. 10)
      • B.R. Bai , B. Chen , H.M. Wang . Syllable-based Chinese text/spoken document retrieval using text/speech queries. Int. J. Pattern Recogni. Artif. Intell. , 5 , 603 - 616
    11. 11)
    12. 12)
      • Y. Itoh , K. Tanaka , S. Lee . An algorithm for similar utterance section extraction for managing spoken documents. Multimedia Syst. , 5 , 432 - 443
    13. 13)
      • Itoh, Y.: `A matching algorithm between arbitrary sections of two speech data sets for speech retrieval', Proc. Int. Conf. Acoustics, Speech, and Signal Processing, 2001, p. 593–596.
    14. 14)
      • Itoh, Y., Tanaka, K.: `Speech labeling and the most frequent phrase extraction using same section in a presentation speech', Proc. Int. Conf. Acoustics, Speech, and Signal Processing, 2002, p. I‐737–I‐740.
    15. 15)
      • M. Tomczak . Spatial interpolation and its uncertainty using automated anisotropic inverse distance weighting (IDW) cross-validation/jackknife approach. J. Geogr. Inf. Decis. Anal. , 2 , 18 - 30
    16. 16)
    17. 17)
      • L. Rabiner , B. Juang . (1993) Fundamentals of speech recognition.
    18. 18)
      • R. Baeza-Yates , B. Ribeiro-Neto . (1999) Modern information retrieval.
    19. 19)
      • Lo, W.K., Meng, H., Ching, P.C.: `Multi-scale and multi-model integration for improved performance in Chinese spoken document retrieval', Proc. Int. Conf. Spoken Language Processing, 2002, p. 1513–1516.
    20. 20)
      • ‘Intel PXA255 processors developer's manual’, available at: http://www.intel.com accessed 2004.
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