Bio Tech Engineering – Paper Presentation Topics

Bio Tech Engineering – Paper Prasentation Topics

Bio Tech Engineering - Paper Prasentation Topics

Bio Tech Engineering – Paper Prasentation Topics search engine Keywords Thesis 123

  • Bio Tech Engineering – Paper Presentation Topics
  • Bio Tech Engineering
  • Paper Presentation Topics
  • Speech recognition technology
  • voice response system
  • Linear Predictive Coding
  • electronic phone book
  • dynamic features spanning several segments
  • voice source characteristics
  • first and second order
  • cepstral coefficients
  • biotechnology research topics
  • biotechnology research topics list
  • recent research in biotechnology
  • biotech topics
  • biotechnology project topics list

Bio Tech Engineering – Paper Prasentation Topics

Speech recognition technology is used more and more for telephone applications like travel booking and information, financial account information, customer service call routing, and directory assistance. Using constrained grammar recognition, such applications can achieve remarkably high accuracy. Research and development in speech recognition technology has continued to grow as the cost for implementing such voice-activated systems has dropped and the usefulness and efficacy of these systems has improved. For example, recognition systems optimized for telephone applications can often supply information about the confidence of a particular recognition, and if the confidence is low, it can trigger the application to prompt callers to confirm or repeat their request. Furthermore, speech recognition has enabled the automation of certain applications that are not automatable using push-button interactive voice response (IVR) systems, like directory assistance and systems that allow callers to “dial” by speaking names listed in an electronic phone book.

Speaker identity is correlated with the physiological and behavioral characteristics of the speaker. These characteristics exist both in the spectral envelope (vocal tract characteristics) and in the supra-segmental features (voice source characteristics and dynamic features spanning several segments). The most common short-term spectral measurements currently used are Linear Predictive Coding (LPC)-derived cepstral coefficients and their regression coefficients. A spectral envelope reconstructed from a truncated set of cepstral coefficients is much smoother than one reconstructed from LPC coefficients. Therefore it provides a stabler representation from one repetition to another of a particular speaker’s utterances. As for the regression coefficients, typically the first- and second-order coefficients are extracted at every frame period to represent the spectral dynamics. These coefficients are derivatives of the time functions of the cepstral coefficients and are respectively called the delta- and delta-delta-cepstral coefficients