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

By Leena Mary

ISBN-10: 1461411580

ISBN-13: 9781461411581

ISBN-10: 1461411599

ISBN-13: 9781461411598

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

  • The importance of prosody for speech processing applications
  • Why prosody must be included in speech processing applications
  • Different equipment for extraction and illustration of prosody for purposes comparable to speech synthesis, speaker attractiveness, language reputation and speech recognition

This booklet 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

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5 0 Time(s) Fig. 2 (a) Speech waveform with manual marked VOPs, (b) Hilbert envelope of LP residual, (c) VOP evidence plot, (d) Output of peak picking algorithm (e) Hypothesized VOP after eliminating few spurious peaks. hence can be used as a cue for detecting VOP. The instant with maximum excitation within a pitch period corresponds to the instant of glottal closure. The places with significant change in the strength of excitation give the evidence for the detection of VOPs [42]. Fig. 2 Extraction and representation of prosodic features in ASR free approaches 23 F0 VOP locations Frames Speech signal Extraction of F0 Smoothing of F0 Contour Association of VOP and F0 Contour Feature Extraction Features Extraction of VOP Fig.

The approach of prosodic attribute model for LID make use of Vector Space Models (VSM) to train language recognizers [84]. This prosodic LID system with PAM is evaluated in NIST Language Recognition Evaluations (LRE) 2007 and 2009 gave respectively 21% and 11% relative EER reduction, while fusing with the scores of phonotactic LID system. The contributions of prosodic features in detecting some of the target languages, including tonal languages, are even more substantial. 2 gives a summary of prosodic features and modeling techniques used for language recognition.

Each syllable is assigned a tone represented by a variable T. T can take any value from 1 to 5, each number denoting a specific tone in Mandarin Chinese. Lexical word boundaries are represented by variable B which can take 1 (indicating a lexical word boundary) or 0 (indicating a syllable boundary which is not a lexical word boundary). Prosodic features are derived from pitch, duration and energy. Various pitch related features used include the average value of the pitch within the syllable, the average of the absolute value of the pitch slope within the syllable, the range of the pitch within the syllable and the pitch reset across the boundary.

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


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