17 research outputs found
Model-based Speech Enhancement for Intelligibility Improvement in Binaural Hearing Aids
Speech intelligibility is often severely degraded among hearing impaired
individuals in situations such as the cocktail party scenario. The performance
of the current hearing aid technology has been observed to be limited in these
scenarios. In this paper, we propose a binaural speech enhancement framework
that takes into consideration the speech production model. The enhancement
framework proposed here is based on the Kalman filter that allows us to take
the speech production dynamics into account during the enhancement process. The
usage of a Kalman filter requires the estimation of clean speech and noise
short term predictor (STP) parameters, and the clean speech pitch parameters.
In this work, a binaural codebook-based method is proposed for estimating the
STP parameters, and a directional pitch estimator based on the harmonic model
and maximum likelihood principle is used to estimate the pitch parameters. The
proposed method for estimating the STP and pitch parameters jointly uses the
information from left and right ears, leading to a more robust estimation of
the filter parameters. Objective measures such as PESQ and STOI have been used
to evaluate the enhancement framework in different acoustic scenarios
representative of the cocktail party scenario. We have also conducted
subjective listening tests on a set of nine normal hearing subjects, to
evaluate the performance in terms of intelligibility and quality improvement.
The listening tests show that the proposed algorithm, even with access to only
a single channel noisy observation, significantly improves the overall speech
quality, and the speech intelligibility by up to 15%.Comment: after revisio