Deep neural network (DNN)-based speech enhancement models often face challenges in maintaining their performance for speakers not encountered during training. This challenge is exacerbated in applications such as enhancement and bandwidth extension of bone-conducted speech, where the distortion exhibits a close correlation with speaker-specific characteristics. We address this issue by introducing a bottleneck module aimed at disentangling speaker-specific characteristics from speech content in speech enhancement DNNs. A DNN model is trained for enhancement of bone-conducted speech and modified with the proposed bottleneck module. We evaluate the DNN’s adaptability to unseen speakers through fine-tuning the network with a limited amount of adaptation data. The results show that the proposed bottleneck module can enhance adaptation performance to new unseen speakers, especially when limited amount of speaker-specific adaptation data is available.

We propose a monaural intrusive speech intelligibility prediction (SIP) algorithm called STGI based on detecting glimpses in short-time segments in a spectro-temporal modulation decomposition of the input speech signals. Unlike existing glimpse-based SIP methods, the application of STGI is not limited to additive uncorrelated noise; STGI can be employed in a broad range of degradation conditions. Our results show that STGI performs consistently well across 15 datasets covering degradation conditions including modulated noise, noise reduction processing, reverberation, near-end listening enhancement, checkerboard noise, and gated noise.

Spectro-temporal modulations are believed to mediate the analysis of speech sounds in the human primary auditory cortex. Inspired by humans' robustness in comprehending speech in challenging acoustic environments, we propose an intrusive speech intelligibility prediction (SIP) algorithm, wSTMI, for normal-hearing listeners based on spectro-temporal modulation analysis (STMA) of the clean and degraded speech signals. In the STMA, each of 55 modulation frequency channels contributes an intermediate intelligibility measure. A sparse linear model with parameters optimized using Lasso regression results in combining the intermediate measures of 8 of the most salient channels for SIP. In comparison with a suite of 10 SIP algorithms, wSTMI performs consistently well across 13 datasets, which together cover degradation conditions including modulated noise, noise reduction processing, reverberation, near-end listening enhancement, and speech interruption. We show that the optimized parameters of wSTMI may be interpreted in terms of modulation transfer functions of the human auditory system. Thus, the proposed approach offers evidence affirming previous studies of the perceptual characteristics underlying speech signal intelligibility.

Several recent high-performing intelligibility estimators of acoustically degraded speech signals employ temporal modulation analysis. In this paper, we investigate the utility of using both spectro- and temporal-modulation for estimating speech intelligibility. We modified a pre-existing speech intelligibility estimation scheme (STMI) that was inspired by human auditory spectro-temporal modulation analysis. We produced several variants of the modified STMI and assessed their intelligibility prediction accuracy, in comparison with several high-performing estimators. Among the estimators tested, one of the STMI variants and eSTOI performed consistently well on both noisy and reverberated speech. These results suggest that spectro-temporal modulation analysis is useful for certain degradation conditions such as modulated noise and reverberation.

Speech is a rapidly varying signal. Temporal processing generally slows with age and many older adults experience difficulties in understanding speech. This research involved over 250 young, middle-aged and older listeners. Temporal processing abilities were assessed in numerous vowel sequence tasks, and analyses examined several factors that might contribute to performance. Significant factors included age and cognitive function as measured by the WAIS-III, but not hearing status for the audible vowels. In addition, learning effects were assessed by retesting two tasks. All groups significantly improved vowel temporal-order identification to a similar degree, but large differences in performance between groups were still observed.