Extraction and Representation of Prosody for Speaker, Speech by Leena Mary

By Leena Mary

Extraction and illustration of Prosodic gains for Speech Processing Applications bargains with prosody from speech processing viewpoint with themes together with:

  • The value of prosody for speech processing applications
  • Why prosody must be included in speech processing applications
  • Different equipment for extraction and illustration of prosody for functions similar to speech synthesis, speaker reputation, language reputation and speech recognition

This ebook is for researchers and scholars on the graduate level.

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Additional info for Extraction and Representation of Prosody for Speaker, Speech and Language Recognition

Example text

The number of units in the input and output layers is equal to the size of the input vectors. The number of units in the middle hidden layer is less than the number of units in the input and output layers, and this layer is called the dimension compression hidden layer. The activation function of the units in the input and output layers are linear, whereas the activation function of the units in the hidden layers can be either linear or nonlinear. The AANN model, with a dimension compression layer in the middle, is used primarily for capturing the distribution of input features in the feature space.

Studies have indicated that listeners are more sensitive to variations in F0 p than F0v [49]. Hence change in F0 (Δ F0 ), distance of F0 peak with reference to VOP (D p ) and peak value of F0 (F0 p ) for each segment of F0 contour may be useful for speaker recognition. An increase in F0 may be obtained by increasing the vocal fold tension, by increasing the subglottal pressure, or a combination of them. Therefore F0 peak (F0 p ) and F0 mean (F0μ ) obtained for each segment of F0 contour may reflect some physiological as well as habitual aspect of a speaker.

The activation function of the units in the input and output layers are linear, whereas the activation function of the units in the hidden layers can be either linear or nonlinear. The AANN model, with a dimension compression layer in the middle, is used primarily for capturing the distribution of input features in the feature space. The ability of AANN models to estimate arbitrary densities has been demonstrated [70]. While testing, the output of the AANN model is computed with input test vector, and the squared error with respect to the output vector is calculated for each input 3 Modeling and Integration of Prosody for Speaker, Language and Speech Recognition 4 2 INPUT 1 .

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