The Hidden Markov Model (HMM) has been widely used in many applications such as speech recognition. A common challenge for applying the classical HMM is to determine the structure...
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...
A system for musical accompaniment is presented in which a computer-driven orchestra follows and learns from a soloist in a concerto-like setting. The system is decomposed into th...
Abstract. In recent years, there has been a great deal of work in modeling audio using non-negative matrix factorization and its probabilistic counterparts as they yield rich model...
In part of speech tagging by Hidden Markov Model, a statistical model is used to assign grammatical categories to words in a text. Early work in the field relied on a corpus which...
This paper presents a statistical, learned approach to finding names and other nonrecursive entities in text (as per the MUC-6 definition of the NE task), using a variant of the s...
Daniel M. Bikel, Scott Miller, Richard M. Schwartz...
A novel method to model and predict the location and orientation of alpha helices in membrane- spanning proteins is presented. It is based on a hidden Markov model (HMM) with an a...
Erik L. L. Sonnhammer, Gunnar von Heijne, Anders K...
A hidden Markov model of signal peptides has been developed. It contains submodels for the N-terminal part, the hydrophobic region, and the region around the cleavage site. For kn...
This paper describes the conversion of a Hidden Markov Model into a sequential transducer that closely approximates the behavior of the stochastic model. This transformation is es...
This paper presents a hybrid approach for named entity (NE) tagging which combines Maximum Entropy Model (MaxEnt), Hidden Markov Model (HMM) and handcrafted grammatical rules. Eac...