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We propose a probabilistic, non-intrusive method for quality assessment of speech that takes into consideration the bounded character of the preference scores. The quality ratings are modeled as iid Beta random variables, whose mean and precision are parametrized directly in terms of the signal features. Maximum likelihood estimation is used to learn the model parameters in view of a training database...
In speaker identification, most of the computational processing time is required to calculate the likelihood of the test utterance of the unknown speaker with respect to the speaker models in the database. When number of speakers in the database is in the order of 10,000 or more, then computational complexity becomes very high. In this paper, we propose a Maximum Likelihood Linear Regression (MLLR)...
Quantitative evaluation of the quality of a speaker's pronunciation of the vowels of a language can contribute to the important task of speaker accent detection. Our aim is to qualitatively and quantitatively distinguish between native and non-native speakers of a language on the basis of a comparative study of two analysis methods. One deals with relative positions of their vowels in formant (F1-F2)...
This paper considers a method for speech emotion recognition by a max-margin framework incorporating a loss function based on a well-known model called theWatson and Tellegen's emotion model. Each emotion is modeled by a single-state hidden Markov model (HMM) that is trained by maximizing the minimum separation margin between emotions, and the margin is scaled by a loss function. The framework is...
A new method based on discriminative model combination for acoustic model training is proposed. The MPE trained model and the MMIE trained model are used for model combination. The combination criterion is based on the ratio of the inter-variance to the intra-variance of each model. Besides we also propose a ldquoclusterrdquo method for the model to choose its confusion models in order to get the...
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