The Baum–Welch algorithm uses the well known EM algorithm to find the maximum likelihood estimate of the parameters of a hidden Markov model given a set of observed feature vectors.

Viterbi's algorithm is used in most communication devices (e.g., cell phones, wireless network cards, etc.) to decode messages in noisy channels; it also has widespread applications in speech recognition.

There are four typical ways of learning the observation probabilities, bj(~x). Vector quantize ~x, using some VQ method. Suppose ~x is the kth codevector; then we just need to learn bj(k) such that. …

Jul 21, 2019 · In this article, we will talk about the algorithm for training up a HMM, before making use of it for prediction. Also known as the forward-backward algorithm, the Baum-Welch algorithm is a...

To solve this problem, we employ an expectation-maximization algorithm known as the Baum-Welch algorithm. The idea is that the algorithm alternates between calculating the expected number of …

Jun 13, 2025 · Dive into the details of the Baum-Welch algorithm, exploring its role in HMM parameter estimation and its impact on Algorithm Design.

This curious phenomenon has been observed in other contexts (Chaganty and Liang, 2013), but has not been explained to date. Obtaining a theoretical characterization of when and why the Baum-Welch …

The Baum-Welch algorithm, also known as the expectation-maximization (EM) algorithm, is a method used in computer science to iteratively refine initial estimates of Hidden Markov Model (HMM) …

The Baum-Welch algorithm is often cited from their paper titled “A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains” (1970), which laid the foundation …

Jan 31, 2022 · The TLDR is this: if you truly have no labeled data and no knowledge of anything you can use the Baum-Welch algorithm to fit an HMM. But for technical reasons the Baum-Welch algorithm …