Investigation on the Characteristics of Long Term Average Spectrum from Human Speech
Date Issued
2011
Date
2011
Author(s)
Lin, Zhan-Yi
Abstract
The unique timbres of different speakers make their speech discriminative. There have been many algorithms trying to quantify the timbre characteristics for speaker identification systems. Long Term Average Spectrum (LTAS), an averaged spectrum on a long term series of the human speech, is one of the most popular technologies to analyze speakers’ characteristics. LTAS is considered to disregard the influence of contents but keep only speakers’ characteristics, and it has been used in many applications on human speech analysis and recognition.
In this thesis, the characteristics of the LTAS are analyzed. Experiments demonstrated that the previous arguments on LTAS might only hold in particular situations. LTAS cannot totally disregard the influence of the contents in general. It is improper for speaker identification unless embeds the same content distribution. LTAS somehow represents the speakers’ characteristics, but the content distribution should be considered at the same time. So the previous applications based on the LTAS might be improved.
Subjects
Long Term Average Spectrum
Speaker Recognition
Type
thesis
File(s)![Thumbnail Image]()
Loading...
Name
ntu-100-R98921058-1.pdf
Size
23.32 KB
Format
Adobe PDF
Checksum
(MD5):ff2a9d345bae89a4d2e4ae278a647aaf