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The Baum-Welch algorithm uses the EM (Expectation Maximization) algorithm to estimate the model parameters $(T, E, \pi)$, where: $T$: the transition probabilities $E$: the emition probabilities $\pi$: probability distribution on the states Some years ago, I made the following quick-and-dirty implementation (may be fairly broken now), for the discrete ...


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Not only do female and male speakers produce different MFCCs, but each speaker will produce quite different MFCCs for the same word, depending on pitch, vocal tract, accent, and many more factors. The important thing is not that MFCCs be independent of gender or any other feature, but that the trained models are independent of that feature. And that is a ...


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My current understanding is that each of the phones in a triphone corresponds to each of the states. That's not correct. Each phone, regardless of the context dependency, has three states: "start", "in-between", and "end". There are always transitions from the previous state to the next one and to itself. This defines the structure of HMM. Now context-...


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I guess you confuse GMM and HMM trainings. Although in both cases EM algorithm is employed, Baum-Welch is used for HMM training.


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For observations people usually use a mixture of gaussians, not a simple gaussian. They have few advantages - EM algorithm is fast to converge and GMM approximates wide variety of distributions pretty well. Probability of the model can be computed efficiently with gaussian selection. Last, GMMs are easy to cluster for context-dependent tree for phonetic ...


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