Intelligibility listening tests are necessary during development and evaluation of speech
processing algorithms, despite the fact that they are expensive and time consuming. In this …

Speech Intelligibility Prediction (SIP) algorithms are becoming popular tools within the
development and operation of speech processing devices and algorithms. However, many …

The calculation of most objective speech intelligibility assessment metrics requires clean
speech as a reference. Such a requirement may limit the applicability of these metrics in real …

Speech intelligibility prediction methods have recently gained popularity in the speech
processing community as supplements to time consuming and costly listening experiments …

Instrumental intelligibility metrics are commonly used as an alternative to listening tests. This
paper evaluates 12 monaural intrusive intelligibility metrics: SII, HEGP, CSII, HASPI, NCM …

We propose a non-intrusive intelligibility measure for noisy and non-linearly processed
speech, ie a measure which can predict intelligibility from a degraded speech signal without …

Objective speech intelligibility measures are gaining popularity in the development of
speech enhancement algorithms and speech processing devices such as hearing aids …

The majority of deep neural network (DNN) based speech enhancement algorithms rely on
the mean-square error (MSE) criterion of short-time spectral amplitudes (STSA), which has …

Most current very low bit rate (VLBR) speech coding systems use hidden Markov model
(HMM) based speech recognition and synthesis techniques. This allows transmission of …

In recent years, there has been an increasing interest in objective measures of speech
intelligibility in the speech processing community. Important progress has been made in …