Noise Robust Speaker Recognition using Reduced Multiconditional Gaussian Mixture Models
نویسندگان
چکیده
منابع مشابه
Robust text-independent speaker identification using Gaussian mixture speaker models
This paper introduces and motivates the use of Gaussian mixture models (CMM) for robust text-independent speaker identification. The individual Gaussian components of a GMM are shown to represent some general speaker-dependent spectral shapes that are efTective for modeling speaker identity. The focus of this work is on applications which require high identification rates using short utterance ...
متن کاملAutomatic Speaker Recognition Using Gaussian Mixture Speaker Models
• Speech conveys several levels of information. On a primary level, speech conveys the words or message being spoken, but on a secondary level, speech also reveals information about the speaker. The Speech Systems Technology group at Lincoln Laboratory has developed and experimented with approaches for automatically recognizing the words being spoken, the language being spoken, and the topic of...
متن کاملFuzzy Gaussian mixture models for speaker recognition
A fuzzy clustering based modification of Gaussian mixture models (GMMs) for speaker recognition is proposed. In this modification, fuzzy mixture weights are introduced by redefining the distances used in the fuzzy c-means (FCM) functionals. Their reestimation formulas are proved by minimising the FCM functionals. The experimental results show that the fuzzy GMMs can be used in speaker recogniti...
متن کاملSkew Gaussian Mixture Models for Speaker Recognition
The current paper proposes skew Gaussian mixture models for speaker recognition and an associated algorithm for its training from experimental data. Speaker identification experiments were conducted, in which speakers were modeled using the familiar Gaussian mixture models (GMM), and the new skewGMM. Each model type was evaluated using two sets of feature vectors, the mel-frequency cepstral coe...
متن کاملImproving Speaker Recognition Performance Using Phonetically Structured Gaussian Mixture Models
Throughout the past few years it has been shown that Gaussian Mixture Models (GMM) are highly suitable for speaker identification and verification. Nevertheless these models try to represent primarily the distribution of the available training data neglecting any possible phonetic information which might be of worth. In our paper we present a recognition system using multiple speaker GMMs based...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: The International Journal of Forensic Computer Science
سال: 2008
ISSN: 1809-9807,1980-7333
DOI: 10.5769/j200801006