A new method for estimating parameters in hidden Markov models has been developed. This method uses a fast-converging approach based on quasi-Newton optimization methods and a recursive algorithm to calculate the likelihood and its derivative efficiently. This technique offers an alternative to the commonly used forward-backward algorithm, providing a quicker way to estimate HMM parameters for applications like speech recognition and digital communications.