Evaluation of Fundamental Validity in Applying AR-HMM with Automatic Topology Generation to Pathology Voice Analysis

摘要

Voice-pathology detection from a subject's voice is a promising technology for the pre-diagnosis of larynx diseases. Glottal source estimation in particular plays a very important role in voice-pathology analysis. To more accurately estimate the spectral envelope and glottal source of the pathology voice, we propose a method that can automatically generate the topology of the Glottal Source Hidden Markov Model (GS- HMM), as well as estimate the Auto-Regressive (AR)-HMM parameter by combining the AR-HMM parameter estimation method and the Minimum Description Length-based Successive State Splitting (MDL-SSS) algorithm. This paper evaluates the fundamental validity of pathology-voice analysis based on the proposed method. The experiment results confirmed the feasibility and fundamental validity of the proposed method.