Hierarchical Command Recognition Based on Large Margin Hidden Markov Models
نویسنده
چکیده
The dominant role of Hidden Markov Models (HMMs) in automatic speech recognition (ASR) is not to be denied. At first, the HMMs were trained using the Maximum Likelihood (ML) approach, using the BaumWelch or Expectation Maximization algorithms (Rabiner, 1989). Then, discriminative training methods emerged, i.e. the Minimum Classification Error (Sha & Saul, 2007; Siohan et al., 1998), the Conditional Maximum Likelihood, the Maximum Mutual Information (Bahl et al., 1986), the Maximum Entropy (Kuo & Gao, 2006; Macherey & Ney, 2003) and the Large Margin (LM) approach (Jiang et al., 2006; Sha & Saul, 2007). These methods enabled an improvement of class separation (e.g. phonemes or words), but generally suffered from computational complexity, slow convergence or ill conditioning of computational algorithms. In this work the Large Margin HMMs are used, but the training algorithm is based on the iterative use of the well conditioned Baum Welch algorithm, so there are no problems with its convergence. Such a corrective HMM training yields an improvement of class separation, which is tested on the speaker independent commands recognition and the spoken digits recognition tasks. This text is partially based on the publication (Dymarski & Wydra, 2008), but it contains new concepts and not yet published results, e.g. the corrective training approach is extended to simultaneous design of a whole set of HMMs (not only two), the selective optimization concept is presented and the hierarchical command recognition system is designed and tested.
منابع مشابه
Hierarchical Command Recognition Based on Large Margin Hidden Markov Models 5
The dominant role of Hidden Markov Models (HMMs) in automatic speech recognition (ASR) is not to be denied. At first, the HMMs were trained using the Maximum Likelihood (ML) approach, using the BaumWelch or Expectation Maximization algorithms (Rabiner, 1989). Then, discriminative training methods emerged, i.e. the Minimum Classification Error (Sha & Saul, 2007; Siohan et al., 1998), the Conditi...
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