Home Technique • Download Incorporating Knowledge Sources into Statistical Speech by Sakriani Sakti, Konstantin Markov, Satoshi Nakamura, PDF

Download Incorporating Knowledge Sources into Statistical Speech by Sakriani Sakti, Konstantin Markov, Satoshi Nakamura, PDF

By Sakriani Sakti, Konstantin Markov, Satoshi Nakamura, Wolfgang Minker

Incorporating wisdom resources into Statistical Speech Recognition deals recommendations for reinforcing the robustness of a statistical computerized speech reputation (ASR) approach via incorporating numerous extra wisdom assets whereas protecting the educational and popularity attempt possible.

The authors offer a good basic framework for incorporating wisdom resources into cutting-edge statistical ASR structures. This framework, referred to as GFIKS (graphical framework to include extra wisdom sources), was once designed by using the concept that of the Bayesian community (BN) framework. This framework permits probabilistic relationships between various details assets to be realized, different types of wisdom resources to be integrated, and a probabilistic functionality of the version to be formulated.

Incorporating wisdom assets into Statistical Speech Recognition demonstrates how the statistical speech reputation method might contain more information assets through the use of GFIKS at diversified degrees of ASR. The incorporation of varied wisdom assets, together with history noises, accessory, gender and huge phonetic wisdom details, in modeling is mentioned theoretically and analyzed experimentally.

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3. q1 q2 q3 HMM state Mixtue weight w jk Continuous Gaussian component Fig. 19. Continuous GMM, where the continuous observation space is modeled using mixture Gaussians (state-specific). They are weighted and added to compute the emission statistic likelihoods (HMM state output probabilities). ) is a Gaussian function with mean vector µjk and covariance matrix Σjk . The HMM segmental likelihood, P (Xs |λ), is then calculated from the joint probability of observation and the state sequence, taken over all state sequences (total likelihood) or approximately over just the most likely state sequence (Viterbi path) (Holmes and Huckvale, 1994).

2 Theory of Hidden Markov Models 27 • Recursive forward calculation A more efficient algorithm, the so-called forward algorithm, can be used to calculate P (Xs |λ). , 2001). , xt ): αt (j) = P (xt1 , qt = j|λ). 12) αt (j) = αt−1 (i)aij bj (xt ); for 2 ≤ t ≤ T ; 1 ≤ j ≤ N,  i=1 which terminates in the final column: N P (Xs |λ) = αT (j). 7 shows the induction step graphically. Since the probability at each cell t − 1 has been computed before proceeding to t, the complexity for the forward algorithm is only O(N 2 T ), rather than exponential.

A set of distinct observation symbols for each state that correspond to the physical output of the system being modeled. , xT ; where xt = the observed output at time t. 3. The transition probabilities from state i to state j A = {aij }; where aij = P (qt = j|qt−1 = i). 4. The observation symbol probability distribution in state j B = {bj (xt )}; where bj (xt ) = P (xt |qt = j). 5. The initial state distribution π = {πi }; where πi = P (q1 = i). It can be seen that a complete definition of an HMM requires specification of two model parameters (N and M ), specification of observation symbols, and specification of three probability measures A, B, and π.

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