Amazon.com: 1999 IEEE International Conference on Acoustics, Speech and Signal Processing (Icassp) (9780780350410): IEEE Signal Processing Society, IEEE, Institute of Electrical and Electronics: Books


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1999 IEEE International Conference on Acoustics, Speech and Signal Processing (Icassp)
 
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1999 IEEE International Conference on Acoustics, Speech and Signal Processing (Icassp) [Paperback]

IEEE Signal Processing Society (Author), IEEE (Author), Institute of Electrical and Electronics (Editor)
4.0 out of 5 stars  See all reviews (1 customer review)

Price: $470.00
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Product Details

  • Paperback: 3584 pages
  • Publisher: Ieee; 1999 edition (February 1999)
  • Language: English
  • ISBN-10: 0780350413
  • ISBN-13: 978-0780350410
  • Product Dimensions: 11 x 9.5 x 8.5 inches
  • Shipping Weight: 20.6 pounds (View shipping rates and policies)
  • Average Customer Review: 4.0 out of 5 stars  See all reviews (1 customer review)
  • Amazon Best Sellers Rank: #9,682,553 in Books (See Top 100 in Books)

 

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4 of 4 people found the following review helpful:
4.0 out of 5 stars Automatic Speech Recognition in 1999, January 11, 2004
This review is from: 1999 IEEE International Conference on Acoustics, Speech and Signal Processing (Icassp) (Paperback)
This book's most interesting sections describe the state of the art in 1999 of automatic speech recognition. The dominance of Hidden Markov Models and Linear Predictive Coding had been clear by then, for practical, near-realtime ASRs.

There is reference in several sections to the high speed microprocessors and cheap memory and disk space that made all this possible. Given that this is 2004 and the book came out in 1999, it means that in the described research, typically the CPU clocks were under 1 GHz, and memory was maybe less than 100 Mb. In other words, with current hardware, you should be able to implement most of the described algorithms with possibly greater efficacy than achieved in the text. The other implication is that now there might be greater commercial applications possible using these methods.

There is some discussion of how to improve ASR recognition. But this can run into the currently intractable problem of trying to infer semantics from an arbitrary utterance. Hard AI.

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