# Listeners' Spectral Reallocation Preferences for Speech in Noise
## 1 Introduction
Reallocating energy to the mid-frequencies (1-3 kHz) using spectral tilt adjustment [41; 42], high-pass filtering [43; 44] or formant amplitude equalisation [45] has been shown to lead to substantial intelligibility increases in noise. For example, [PERSON] and [PERSON] [44] demonstrated that high-pass filtering led to gains of 45 and 55 percentage points for keyword identification in sentences in high and low SNRs, respectively. While other forms of speech modification (e.g., durational or modulation adjustments) are also worth investigating, the potential gains from the above-cited studies motivated our focus on spectral manipulation in the present study. Spectral energy reallocation is both simple to implement and highly effective in boosting intelligibility. However, while the delivery of a fully comprehensible message is of paramount importance, other factors, such as naturalness, quality and listening effort, are known to influence a listener's overall experience (e.g., [46; 47; 48]). Relatively little is known about the impact of speech modification on these factors. Nevertheless, a few studies have examined the effect of certain types of speech modification on speech quality and listening effort. These investigations have demonstrated that modifications which successfully promote intelligibility can also have the side effect of reducing speech quality [49], especially when presented in quiet or at comfortable SNRs [50]. Cognitive effort has been shown to increase when listening to synthetic voices, relative to natural speech [51; 52; 53]. On the other hand, decreases in listening effort have been observed for two forms of algorithmically modified speech. Using effort ratings scales, [PERSON] et al. [54] demonstrated that the AdaptDRC algorithm [7] resulted in lower effort than unprocessed speech, while both subjective ratings and pupillometry measures in a study by [PERSON] et al. [53] pointed to a reduced listening effort for the SSDRC algorithm [5]. Collectively, these studies suggest that intelligibility-enhancing speech modifications can have additional impacts--both positive and negative--on a listener's overall experience when processing speech. Intelligibility is easy to measure but assessing a listener's overall speech communication experience is more challenging. Techniques such as ratings scales and questionnaires (e.g., [54; 55]), pupillometry (e.g., [56]) and dual-task performance (e.g., [57; 58]) have been employed to measure aspects beyond intelligibility (for a review, see [59]). An alternative paradigm is used in the present study to investigate the wider impact of spectral energy reallocation. The approach is based on providing listeners with the ability to modify some facet of the speech signal, with real-time auditory feedback, allowing listeners to express their preferences directly. This listener-centric technique has been used in the past to explore listeners' preferred choice of formant frequency/fundamental frequency relationship [60], speech rate [61; 62; 63], speech level [64] and local SNR for speech enhancement [65]. Listener preferences in the context of spectral modifications have previously been explored for individuals with hearing loss by providing them with direct control over broadband, low-, and high-frequency gain [66] or loudness and degree of spectral tilt [67]. A listener-centric approach has a number of potential benefits compared to the more traditional technique, in which behavioural measurements are obtained for a small number of experimentally defined processing conditions. First, it can be used to explore the parameter space at a finer level of quantisation than is economically efficient in traditional designs. Second, compared to the use of rating scales, the approach is likely to be more natural for a listener, being free of the semantic labels that map multi-faceted stimuli on to a finite number of categories. Further, the approach provides an additional objective metric--adjustment time--that may reflect task difficulty.