We present a comparison of 6 methods for classification of sports audio. For the feature extraction we have two choices: MPEG-7 audio features and Mel-scale Frequency Cepstrum Coefficients (MFCC). For the classificaiton we also have two choices: Maximum Likelihood Hidden Markov Models (ML-HMM) and Entropic Prior HMM(EP-HMM). EP-HMM, in turn, have two variations: with and without trimming of the...

In state-of-art large vocabulary continuous speech recognition (LVCSR) systems, HMM state-tying is often used to achieve good balance between the model resolution and robustness. In this paradigm, tied HMM states share a single set of parameters and are nondistinguishable. To capture the fine differences among tied HMM states, a probabilistic classification of HMM states (PCHMM) is proposed in ...

In the present paper, a trajectory model, derived from the hidden Markov model (HMM) by imposing explicit relationships between static and dynamic feature vector sequences, is developed and evaluated. The derived model, named trajectory HMM, can alleviate some limitations of the standard HMM, which are i) piece-wise constant statistics within a state and ii) conditional independence assumption ...