Hardcover: 936 pages
Publisher: Wiley-IEEE Press (October 5, 1999)
Product Dimensions: 6.2 x 2.1 x 9.2 inches
Shipping Weight: 3 pounds (View shipping rates and policies)
Average Customer Review: 4.2 out of 5 stars See all reviews (8 customer reviews)
Best Sellers Rank: #1,071,677 in Books (See Top 100 in Books) #27 in Books > Computers & Technology > Software > Voice Recognition #879 in Books > Reference > Words, Language & Grammar > Speech #905 in Books > Engineering & Transportation > Engineering > Electrical & Electronics > Circuits
This book is very thorough, but at times it seems like the authors go out of their way to keep their discussion on a very theoretical level.Chapter 1: A highly theoretical review of DSP. You need good knowledge of DSP to understand it.Chapter 2: Goes over the human speech production and recognition systems. Here you get some practical info on the spectral and time-domain properties that distinguish speech sounds.Chapter 3: Describes a model of the speech production based on a series of pulses passed through filters that correspond to features of the human speech production system. Practical issues such as which zero and pole values work best are left as an exercise to the reader.Chapter 4: A lot of mathematics relating long-term statistical properties to those of a short frame of speech data. Contains good info on how to find recursive formulas for statistical properties of speech frames. It is a great shame that the authors don't include examples in MATLAB or pseudo code.Chapter 5: Linear Prediction. Discusses a mathematical algorithm for creating a prediction filter that could be used to predict the next value in a series of data. In speech processing we are interested in using the coefficients of this prediction filter to encapsulate the properties of a speech frame. Examples of 1st,, 2nd, and 3rd order filters would have gone along way to illustrate how to implement this. There are some good formulas to measure the degree of similarities between speech frames based on their LP filter coefficients.Chapter 6: Introduces the concept of the cepstrum. Cepstral analysis allows you to de-convolve speech data to separate the excitation source from the vocal tract filter.
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