# Listeners' Spectral Reallocation Preferences for Speech in Noise
## 1 Introduction  
Speech output is in widespread use to deliver information in listening environments such as transport hubs, in vehicles, and in the home, where other sound sources are likely to be co-active. While live speech is still deployed in such contexts, much of the time listeners hear non-live forms of speech, either pre-recorded natural speech that has been produced offline, or speech that has been dynamically generated by synthesis from text. The use of non-live speech provides an opportunity to manipulate the audio signal prior to presentation, usually with the goal of maintaining or enhancing intelligibility in noise [1; 2; 3; 4; 5; 6; 7]. Such algorithms have been shown in large-scale evaluations (e.g., [8; 9]) to be highly effective in boosting intelligibility when applied to natural (e.g., [10]) or synthetic speech (e.g., [11; 12; 13]). Human speech perception is a complex process that involves bringing linguistic knowledge to bear on the available acoustic evidence. A listener's prior experience with the fundamental structural characteristics of the language, including its phonological rules and phonotactic constraints, significantly shapes speech processing, particularly in adverse conditions. Non-native listeners encounter greater challenges in noisy environments compared to native listeners (see [14] for a review), indicating the importance of linguistic familiarity in understanding speech. The spectral and temporal characteristics of speech also play a crucial role in how listeners perceive and comprehend spoken language. Even in the absence of deliberate speech manipulation, various factors are known to contribute to speech intelligibility, including spectral change [15], syllable-scale amplitude modulations [16; 17], and phonologically relevant sampling instants [18]. Spectral properties in particular impact various aspects of speech perception. For instance, the spectral content of speech is closely linked to many important phonemic contrasts e.g., [19; 20; 21; 22], allowing listeners to distinguish between different speech sounds and forming the basis of the accurate recognition of words. Additionally,the temporal evolution of the spectral envelope contributes to the perception of prosody, rhythm and intonation. In noisy situations, acoustic evidence is partially masked or distorted. It has been shown that speech can be understood after manipulations that reduce its spectral resolution [23] or bandwidth [24], remove its mid-frequency energy [25], or restrict it to sparse time-frequency regions [26; 27]. These findings suggest that the processes underlying human speech perception incorporate substantial latitude in the types of acoustic signals that can be considered speech-like, and motivates the search for manipulations that optimise a listener's experience. Several models have been proposed to explain how noise affects intelligibility [28; 29; 30; 31; 32]. One common feature of many models is the notion of signal-to-noise ratio (SNR) in each frequency region (e.g., [1]), with each frequency receiving an importance weighting [30]. These models highlighted the potential for exploring speech modifications that alter the spectral energy balance of speech in noisy conditions. Energy reallocation is one form of manipulation that alters the overall spectral energy balance. The term'reallocation' is used because the effectiveness of speech modification approaches is typically assessed after normalizing the root-mean-square (RMS) energy following modification [8; 9], which has the consequence that attenuation or boosting in one part of the spectrum leads to changes in other spectral regions. Spectral reallocation techniques are informed by perceptual studies that indicate the relative importance of different spectral regions for speech recognition (e.g., [33; 34; 35; 28]), and by measurements of acoustic properties in naturally enhanced speech, such as the speech styles that result from an instruction to speak clearly [36; 37] or from noise immersion (Lombard speech; [38; 39; 40]).