This paper deals with unsupervised model adaptation for speaker recognition. Two adaptation schemes are proposed, the first one is based on a test by test model adaptation and the second one proposes a batch mode, where the adaptation is performed using a set of tests before computing the decision score for each of them. The experiments are conducted thanks to the NIST SRE 2005 database. This p...

This paper investigates unsupervised language model adaptation, from ASR transcripts. N-gram counts from these transcripts can be used either to adapt an existing n-gram model or to build an n-gram model from scratch. Various experimental results are reported on a particular domain adaptation task, namely building a customer care application starting from a general voicemail transcription syste...

This paper proposes a new sliding mode control (SMC)  based model reference adaptive system (MRAS) for sensorless indirect field oriented control (IFOC) of a single-sided linear induction motor (SLIM). The operation of MRAS speed estimators dramatically depends on adaptation mechanism. Fixed-gain PI controller is conventionally used for this purpose which may fail to estimate the speed correctl...

Recent behavioral studies with computational models have revealed motor memory with multiple time scales in sensorimotor adaptation [1]. However, little has been studied about underlying neural correlates of the motor memory. For this, we designed an even-related fMRI experiment where 21 subjects (15 males and 6 females) learned opposing visuomotor rotation (40 ̊ or -40 ̊) tasks in alternating bl...

STATEMENTA study to assess the effect of lavender oil massage on pain perception during first stage labour among primi parturients admitted in selected hospital, Lucknow. Introduction: Pain labor is a nearly universal experience for child bearing women. Labor challenging issue nurses designing intervention protocols. caused primarily by uterine muscle contractions and somewhat pressure cervix. ...

This paper deals with nonparametric density estimation on $\mathbb{R}^2$ from i.i.d. observations. It is assumed that after unknown rotation of the coordinate system coordinates observations are independent random variables whose densities belong to a Hölder class parameters. The minimax and adaptive theories for this structural statistical model developed.