Remark 12.2. If Fs is N(μs, σ 2 s) and Fb ( 6= Fs) is N(μb, σ b ) then it can be easily shown that the problem is identifiable if and only if σs ≤ σb. When σs > σb, the model is not identifiable, an application of Lemma 2.4 gives α0 = α [ 1−(σb/σs) exp ( −σsσb(μb−μs)/2 )] . Thus, α0 increases to α as |μs − μb| tends to infinity. It should be noted that the problem is actually identifiable if we...

We present some properties of mixture and generalized mixture operators, with special stress on their monotonicity. We introduce new sufficient conditions for weighting functions to ensure the monotonicity of the corresponding operators. However, mixture operators, generalized mixture operators neither quasi-arithmetic means weighted by a weighting function need not be non-decreasing operators,...

Nonnative speech recognition is becoming more and more important as many speech applications are deployed world wide. Meanwhile, due to the large population of nonnative speakers, speaker adaptation remains the most practical way for providing high performance speech services. Subspace Gaussian Mixture Model (SGMM) has recently been shown to yield superior performance on various native speech r...

In developing speech recognition based services for any task domain, it is necessary to account for the support of an increasing number of languages over the life of the service. This paper considers a small vocabulary speech recognition task in multiple Indian languages. To configure a multi-lingual system in this task domain, an experimental study is presented using data from two linguistical...

We propose an algorithm for nonparametric estimation for finite mixtures of multivariate random vectors that is not, but that strongly resembles, a true EM algorithm. The vectors are assumed to have independent coordinates conditional upon knowing which mixture component from which they come, but otherwise their density functions are completely unspecified. Sometimes, the density functions may ...

Abundance estimates from animal point-count surveys require accurate estimates of detection probabilities. The standard model for estimating detection from removalsampled point-count surveys assumes that organisms at a survey site are detected at a constant rate; however, this assumption is often not justified. We consider a class of N-mixture models that allows for detection heterogeneity over...

A new concept of a quantum-like mixture model is introduced. It describes the distribution with assumption that point generated by each Gaussian at same time. The improves classification accuracy in machine learning indicating uncertain points should not be assigned to any class. increases on iris data set from 96.67 99.24%.

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