Experimental evaluation of features for robust speaker identification
نویسنده
چکیده
This paper presents an experimental evaluation of different features and channel compensation techniques for robust speaker identification. The goal is to keep all processing and classification steps constant and to vary only the features and compensations used to allow a controlled comparison. A general, maximum-likelihood classifier based on Gaussian mixture densities is used as the classifier and experiments are conducted on the King speech database, a conversational telephone-speech database. The features examined are mel-frequency and linear-frequency filterbank cepstral coefficients, linear prediction cepstral coefficients and perceptual linear prediction (PLP) cepstral coefficients. The channel compensation techniques examined are cepstral mean removal, RASTA processing, and a quadratic trend removal technique. It is shown for this database that performance differences between the basic features is small and the major gains are due to the channel compensation techniques. The best "across-the-divide" recognition accuracy of 92% is obtained for both high order LPC features and bandlimited filterbank features.
منابع مشابه
Offline Language-free Writer Identification based on Speeded-up Robust Features
This article proposes offline language-free writer identification based on speeded-up robust features (SURF), goes through training, enrollment, and identification stages. In all stages, an isotropic Box filter is first used to segment the handwritten text image into word regions (WRs). Then, the SURF descriptors (SUDs) of word region and the corresponding scales and orientations (SOs) are extr...
متن کاملText Independent Speaker Identification with Finite Multivariate Generalized Gaussian Mixture Model with Distant Microphone Speech
An effective and efficient speaker Identification (SI) system requires a robust feature extraction module followed by a speaker modeling scheme for generalized representation of these features. In recent, years Speaker Identification has seen significant advancement, but improvements have tended to be bench marked on the near field speech, ignoring the more realistic setting of far field instru...
متن کاملCodebook Design Method for Noise Robust Speaker Identification based on Genetic Algorithm
In this paper, a novel method of designing a codebook for noise robust speaker identification purpose utilizing Genetic Algorithm has been proposed. Wiener filter has been used to remove the background noises from the source speech utterances. Speech features have been extracted using standard speech parameterization method such as LPC, LPCC, RCC, MFCC, ΔMFCC and ΔΔMFCC. For each of these techn...
متن کاملImproving Speaker Identification Performance by Combining Vocal Tract Features
This paper proposes fusion and addition techniques of vocal tract features such as Mel Frequency Cepstral Coefficients (MFCC) and Dynamic Mel Frequency Cepstral Coefficients (DMFCC) in speaker identification. Feature extraction plays an important role as a front end processing block in Speaker Identification (SI) process. Mel frequency features are used to extract the spectral characteristics o...
متن کاملNoise Robust Speaker Identification Using Sub-Band Weighting in Multi-Band Approach
Recently, many techniques have been proposed to improve speaker identification in noise environments. Among these techniques, we consider the feature recombination technique for the multi-band approach in noise robust speaker identification. The conventional feature recombination technique is very effective in the band-limited noise condition, but in broad-band noise condition, the conventional...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- IEEE Trans. Speech and Audio Processing
دوره 2 شماره
صفحات -
تاریخ انتشار 1994