This paper presents a framework for maximum a posteriori (MAP) speaker adaptation of state duration distributions in hidden Markov models (HMM). Four key issues of MAP estimation, ...
In this paper we present a novel scheme for unstructured audio scene classification that possesses three highly desirable and powerful features: autonomy, scalability, and robust...
Julian Ramos, Sajid M. Siddiqi, Artur Dubrawski, G...
Hidden Markov Models (HMMs) are the most commonly used acoustic model for speech recognition. In HMMs, the probability of successive observations is assumed independent given the ...
This paper presents a unified approach to human activity capturing and recognition. It targets applications such as a speaker walking, turning around, sitting and getting up from ...
— With the principal goal of developing an alternative, relatively simple and tractable pricing framework for accurately reproducing a market implied volatility surface, this pap...