A Speech Enhancement Algorithm based on Non-negative Hidden Markov Model and Kullback-Leibler Divergence

TL;DR

This paper introduces a supervised speech enhancement algorithm combining non-negative matrix factorization and hidden Markov models, leveraging Kullback-Leibler divergence for improved speech quality and intelligibility.

Contribution

It presents a novel NMF-HMM framework with a KL divergence-based observation model and a parallelizable MMSE estimator for efficient online speech enhancement.

Findings

Achieves 5% improvement in STOI over traditional NMF methods.

Attains 0.18 higher PESQ scores indicating better speech quality.

Demonstrates superior performance compared to state-of-the-art methods.

Abstract

In this paper, we propose a novel supervised single-channel speech enhancement method combing the the Kullback-Leibler divergence-based non-negative matrix factorization (NMF) and hidden Markov model (NMF-HMM). With the application of HMM, the temporal dynamics information of speech signals can be taken into account. In the training stage, the sum of Poisson, leading to the KL divergence measure, is used as the observation model for each state of HMM. This ensures that a computationally efficient multiplicative update can be used for the parameter update of the proposed model. In the online enhancement stage, we propose a novel minimum mean-square error (MMSE) estimator for the proposed NMF-HMM. This estimator can be implemented using parallel computing, saving the time complexity. The performance of the proposed algorithm is verified by objective measures. The experimental results show that the proposed strategy achieves better speech enhancement performance than state-of-the-art speech enhancement methods. More specifically, compared with the traditional NMF-based speech enhancement methods, our proposed algorithm achieves a 5\% improvement for short-time objective intelligibility (STOI) and 0.18 improvement for perceptual evaluation of speech quality (PESQ).