Kalman tracking linear predictor for vowel intelligibility enhancement on european portuguese HMM based speech synthesis

Abstract

The recent developments on Hidden Markov Models (HMM) based speech synthesis showed that this is a promising technology fully capable of competing with other established techniques. However some issues still lack a solution. Several authors report an over-smoothing phenomenon on both time and frequencies which decreases naturalness and sometimes intelligibility. In this work we present a new vowel intelligibility enhancement algorithm that uses a discrete Kalman filter (DKF) for tracking frame based parameters. The inter-frame correlations are modelled by an autoregressive structure which provides an underlying time frame dependency and can improve time-frequency resolution. The system's performance has been evaluated using objective and subjective tests and the proposed methodology has led to improved results.

On the development of an automatic voice pleasantness classification and intensity estimation system

“…From an intelligibility perspective we see that its assessment is highly dependent on the proper articulation of the language sounds according to its standard and that it can vary significantly when dialectal variations are involved, even among speakers of the same language. However Amano-Kusumoto and Hosom (2009) and later Coelho et al (2010) showed that parameters such as formant trajectories in vowels and segmental durations have paramount importance in speech intelligibility. From the naturalness point of view we have found references to measures of artefacts or discontinuities (Mayo et al, 2011).…”