By Leena Mary
Extraction and illustration of Prosodic positive aspects for Speech Processing Applications bargains with prosody from speech processing standpoint with subject matters together with:
- The value of prosody for speech processing applications
- Why prosody have to be included in speech processing applications
- Different tools for extraction and illustration of prosody for functions reminiscent of speech synthesis, speaker acceptance, language popularity and speech recognition
This booklet is for researchers and scholars on the graduate level.
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Additional resources for Extraction and Representation of Prosody for Speaker, Speech and Language Recognition
Xn Fig. 2 Structure of a multilayer feedforward neural network with single output. 6) E= ∑ ( f (x, θ ) − a)2 + ∑ ( f (x, θ ) − b)2 N x∈C x∈C2 1 where N is the total number of training samples. 7) where P(x,Ci ), i = 1, 2, is the joint probability density function of the observations x and the class Ci . 8) Let P(x) = P(x,C1 ) + P(x,C2 ) denote the unconditional probability of an observation. 11) Only the first term in the above equation depends on the parameters of the network. Therefore adjusting the network parameters θ to minimize E is equivalent to minimizing the mean square error between the network output f (x, θ ) and d(x).
Since the specific interaction between pitch variations, intensity and duration play an important role in determining the prosody, the parameters representing F0 contour, duration and energy are combined together to represent prosody. 2 Using inflections or start/end of voicing As illustrated in Fig. 6, the utterance is segmented at inflection points of the temporal trajectories of F0 or at the start or end of voicing . 2 Extraction and representation of prosodic features in ASR free approaches 27 Frequency Raw Pitch Stylized Pitch One Segment + _ UV + _ + _ + _ _ _ 1 4 2 2 Frequency Energy Linear Fit Labels One Segment _ _ 2 4 UV 5 Time Fig.
Then labeling is done based on the nature of the pitch and energy slope as illustrated in Fig. 6. In a similar manner, slope durations are also labeled as Short (S), Medium (M) and Long (L) where quantization boundaries are estimated by the cumulative distribution function of the slope duration. These duration labels can be integrated to the slope labels. For Fig. 6, the sequence of labels are 2L, 4L, 5M, 1M, 4M, 2M, 2S. The effectiveness of these features has been demonstrated for NIST SRE task .