Reverberation usually degrades speech intelligibility for spatially separated speech and noise sources since spatial unmasking is reduced and late reflections decrease the fidelity of the received speech signal. The latter effect could not satisfactorily be predicted by a recently presented binaural speech intelligibility model [Beutelmann et al. (2010). J. Acoust. Soc. Am. 127, 2479–2497]. This study therefore evaluated three extensions of the model to improve its predictions: (1) an extension of the speech intelligibility index based on modulation transfer functions, (2) a correction factor based on the room acoustical quantity “definition,” and (3) a separation of the speech signal into useful and detrimental parts. The predictions were compared to results of two experiments in which speech reception thresholds were measured in a reverberant room in quiet and in the presence of a noise source for listeners with normal hearing. All extensions yielded better predictions than the original model when the influence of reverberation was strong, while predictions were similar for conditions with less reverberation. Although model (3) differed substantially in the assumed interaction of binaural processing and early reflections, its predictions were very similar to model (2) that achieved the best fit to the data.

In order to study the interaction between the intelligibility advantage in rooms due to the presence of early reflections and due to binaural unmasking, a series of speech reception threshold experiments was performed employing a single reflection of the frontal target speech source as a function of its delay ranging from 0 to 200 ms. The direction of the reflection and the spatial characteristic of the interfering noise (diotic, diffuse, or laterally localized) were varied in the experiments. For the frontal reflection, full temporal integration was observed for all three noise types up to a delay of at least 25 ms followed by gradual intelligibility decay at longer delays. At 200 ms delay the reflection introduced additional intelligibility deterioration. For short delays, intelligibility was not reduced when the reflection was spatially separated from the direct sound in the diffuse and lateral noise conditions. A release from the deterioration effect at 200 ms delay was found for all spatially separated reflections. The suppression of a detrimental reflection was symmetrical in diffuse noise, but azimuth-dependent in lateral noise. This indicates an interaction of spatial and temporal processing of speech reflections which challenges existing binaural speech intelligibility models.

Speech perception in complex sound fields can greatly benefit from different unmasking cues to segregate the target from interfering voices. This study investigated the role of three unmasking cues (spatial separation, gender differences, and masker time reversal) on speech intelligibility and perceived listening effort in normal-hearing listeners. Speech intelligibility and categorically scaled listening effort were measured for a female target talker masked by two competing talkers with no unmasking cues or one to three unmasking cues. In addition to natural stimuli, all measurements were also conducted with glimpsed speech—which was created by removing the time–frequency tiles of the speech mixture in which the maskers dominated the mixture—to estimate the relative amounts of informational and energetic masking as well as the effort associated with source segregation. The results showed that all unmasking cues as well as glimpsing improved intelligibility and reduced listening effort and that providing more than one cue was beneficial in overcoming informational masking. The reduction in listening effort due to glimpsing corresponded to increases in signal-to-noise ratio of 8 to 18 dB, indicating that a significant amount of listening effort was devoted to segregating the target from the maskers. Furthermore, the benefit in listening effort for all unmasking cues extended well into the range of positive signal-to-noise ratios at which speech intelligibility was at ceiling, suggesting that listening effort is a useful tool for evaluating speech-on-speech masking conditions at typical conversational levels.

Listening to the audio of TV broadcast signals can be challenging for hearing-impaired as well as normal-hearing listeners, especially when background sounds are prominent or too loud compared to the speech signal. This can result in a reduced satisfaction and increased listening effort of the listeners. Since the broadcast sound is usually premixed, we perform a subjective evaluation for quantifying the potential of speech enhancement systems based on audio source separation and recurrent neural networks (RNN). Recently, RNNs have shown promising resultsin the context of sound source separation and real-time signal processing. In this paper, we separate the speech from the background signals and remix the separated sounds at a higher signal-to-noise ratio. This differs from classic speech enhancement, where usually only the extracted speech signal is exploited. The subjective evaluation with 20 normal-hearing subjects on real TV-broadcast material shows that our proposed enhancement system is able to reduce the listening effort by around 2 points on a 13-point listening effort rating scale and increases the perceived sound quality compared to the original mixture.

This study investigated the effects of different adjustment criteria and sound scenes on self-adjusted hearing-aid gain settings. Self-adjusted settings were evaluated for speech recognition in noi...

Previous studies showed that near-end listening enhancement (NELE) algorithms can significantly improve speech intelligibility in noisy environments. This study investigates the benefit of the NELE algorithm AdaptDRC in normal-hearing listeners at signal-to-noise ratios (SNRs) for which speech intelligibility is at ceiling, by evaluating listening effort for processed and unprocessed speech in the presence of speech-shaped and cafeteria noise. The results suggest that the NELE algorithm is able to reduce listening effort over a wide range of SNRs. Hence, listening effort seems to be applicable for evaluating NELE algorithms over a much wider SNR range than speech intelligibility.