Anchor Models and WCCN Normalization For Speaker Trait Classification
نویسندگان
چکیده
This paper presents an improved version of anchor model applied to solve the two-class classification tasks of the INTERSPEECH 2012 speaker trait Challenge. To build the anchor model space of each task, we include the class models of all tasks. The introduction of within-class covariance normalization (WCCN) applied to the log-likelihood scores of the anchor space not only improves the results compared to the unnormalized version but also exceeds the performance of GMM or GMM-UBM systems. Even if Euclidean distance gives worst performances compared to cosine metric, we find that after normalization both metrics give similar results so they can be used interchangeably.
منابع مشابه
Emotion recognition from children's speech using anchor models
In this paper we have adopted anchor models to solve a multi-class problem of automatic emotion recognition from children's speech. The likelihood scores of an utterance over the emotion models are normalized using their within-class covariance matrix (WCCN) in order to increase the difference in the characteristic behavior of scores between classes. After normalization, we find that WCCN not o...
متن کاملSource normalization for language-independent speaker recognition using i-vectors
Source-normalization (SN) is an effective means of improving the robustness of i-vector-based speaker recognition for under-resourced and unseen cross-speech-source evaluation conditions. The technique of source-normalization estimates directions of undesired within-speaker variation more accurately than traditional methods when cross-source variation is not explicitly observed from each speake...
متن کاملAnalysis of subspace within-class covariance normalization for SVM-based speaker verification
Nuisance attribute projection (NAP) and within-class covariance normalization (WCCN) are two effective techniques for intersession variability compensation in SVM based speaker verification systems. However, by normalizing or removing the nuisance subspace containing the session variability can not guarantee to enlarge the distance between speakers. In this paper, we investigated the probabilit...
متن کاملWithin-class covariance normalization for SVM-based speaker recognition
This paper extends the within-class covariance normalization (WCCN) technique described in [1, 2] for training generalized linear kernels. We describe a practical procedure for applying WCCN to an SVM-based speaker recognition system where the input feature vectors reside in a high-dimensional space. Our approach involves using principal component analysis (PCA) to split the original feature sp...
متن کاملNAP, WCCN, a New Linear Kernel, and Keyword Weighting for the HMM Supervector Speaker Recognition System
We demonstrate the application of Nuisance Attribute Projection (NAP), Within-Class Covariance Normalization (WCCN), a new standard kernel, and keyword weighting for the keywordbased HMM supervector speaker recognition system. On our development set (SRE04 8-side training), we achieve 22.6% and 16.2% EER improvements using NAP and WCCN respectively, a 19.5% EER improvement using NAP and WCCN jo...
متن کامل