Model-Based Speech Enhancement for Intelligibility Improvement in Binaural Hearing Aids

Abstract

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 codebookbased 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%.Index Terms-Kalman filter, binaural enhancement, pitch estimation, autoregressive model. 2329-9290 (c)

RETRACTED: Speech enhancement method using deep learning approach for hearing-impaired listeners

“…[13][14][15]53 In the current trend, machine learning approaches 16 have proved its great strength in speech intelligibility improvement for CI users, 17 NH listeners, HI listeners, [18][19][20] and medical signals. 6,[21][22][23] Normally, based on the incoming signal, gain function for noise and speech statistics is estimated. Nonetheless, the optimal gain function was estimated using the traditional machine learning approaches by means of incorporating prior knowledge of speech and noise patterns.…”

Automatic quality control and enhancement for voice-based remote Parkinson’s disease detection

“…In this section, we used two supervised methods and one unsupervised method to investigate the effect of different noise reduction algorithms in reducing the acoustic mismatch between training and operating conditions. The first supervised enhancement algorithm is based on the framework proposed in [49]. In this approach, a Kalman filter, which takes into account the voiced and unvoiced parts of speech [50], is used for enhancement.…”

“…the fundamental frequency and the degree of voicing). Based on [49], the AR coefficients and excitation variance of the speech and noise are estimated using a codebook-based approach, and the pitch parameters are estimated from the noisy signal using a harmonic model based approach [51]. We refer to this method in the rest of this paper as the Kalman-CB.…”

Fast Nonstationary Noise Tracking Based on Log-Spectral Power MMSE Estimator and Temporal Recursive Averaging

“…where α p (0 < α p < 1) is a smoothing parameter, set to 0.2 in our experiment as adopted in [25]. The smoothing parameter α N is obtained by substituting (8) into (5). Here, using averaged priori SNR reduces random fluctuations inp(l, k), at the same time fast react to changing noise levels is achieved (minimum tracking is abandoned).…”