Towards human-like and transhuman perception in AI 2.0: a review <s> Auditory 2.0: towards auditory perception and computation in an actual auditory setting <s> Abstract This paper reviews past work comparing modern speech recognition systems and humans to determine how far recent dramatic advances in technology have progressed towards the goal of human-like performance. Comparisons use six modern speech corpora with vocabularies ranging from 10 to more than 65,000 words and content ranging from read isolated words to spontaneous conversations. Error rates of machines are often more than an order of magnitude greater than those of humans for quiet, wideband, read speech. Machine performance degrades further below that of humans in noise, with channel variability, and for spontaneous speech. Humans can also recognize quiet, clearly spoken nonsense syllables and nonsense sentences with little high-level grammatical information. These comparisons suggest that the human-machine performance gap can be reduced by basic research on improving low-level acoustic-phonetic modeling, on improving robustness with noise and channel variability, and on more accurately modeling spontaneous speech. <s> BIB001 </s> Towards human-like and transhuman perception in AI 2.0: a review <s> Auditory 2.0: towards auditory perception and computation in an actual auditory setting <s> In a reverberant environment, sounds reach the ears through several paths. Although the direct sound is followed by multiple reflections, which would be audible in isolation, the first-arriving wavefront dominates many aspects of perception. The "precedence effect" refers to a group of phenomena that are thought to be involved in resolving competition for perception and localization between a direct sound and a reflection. This article is divided into five major sections. First, it begins with a review of recent work on psychoacoustics, which divides the phenomena into measurements of fusion, localization dominance, and discrimination suppression. Second, buildup of precedence and breakdown of precedence are discussed. Third measurements in several animal species, developmental changes in humans, and animal studies are described. Fourth, recent physiological measurements that might be helpful in providing a fuller understanding of precedence effects are reviewed. Fifth, a number of psychophysical models are described which illustrate fundamentally different approaches and have distinct advantages and disadvantages. The purpose of this review is to provide a framework within which to describe the effects of precedence and to help in the integration of data from both psychophysical and physiological experiments. It is probably only through the combined efforts of these fields that a full theory of precedence will evolve and useful models will be developed. <s> BIB002 </s> Towards human-like and transhuman perception in AI 2.0: a review <s> Auditory 2.0: towards auditory perception and computation in an actual auditory setting <s> This article presents a review of the effects of adverse conditions (ACs) on the perceptual, linguistic, cognitive, and neurophysiological mechanisms underlying speech recognition. The review starts with a classification of ACs based on their origin: Degradation at the source (production of a noncanonical signal), degradation during signal transmission (interfering signal or medium-induced impoverishment of the target signal), receiver limitations (peripheral, linguistic, cognitive). This is followed by a parallel, yet orthogonal classification of ACs based on the locus of their effect: Perceptual processes, mental representations, attention, and memory functions. We then review the added value that ACs provide for theories of speech recognition, with a focus on fundamental themes in psycholinguistics: Content and format of lexical representations, time-course of lexical access, word segmentation, feed-back in speech perception and recognition, lexicalsemantic integration, interface between the speech system and general cognition, neuroanatomical organisation of speech processing. We conclude by advocating an approach to speech recognition that includes rather than neutralises complex listening environments and individual differences. <s> BIB003
To date, the performance of machines is still far below that of human beings in natural auditory settings, especially in understanding audio in reverberant environments and noisy backgrounds BIB001 . To resolve this problem, it is necessary to study the mechanism of auditory binaural processing, as the binaural advantage is prominent in natural settings with multiple sound sources. Related studies include psychoacoustic models for sound localization and for interpreting the precedence effect BIB002 , adaptive learning models based on HRTF, computational models for multi-scale harmonic analysis, demodulation methods for HRTF, and computational models for sound localization in reverberant environments. Additionally, as speech signals inherently contain rich information and perceptual cues, it is necessary to study the mechanisms underlying the perception of speech with interfering sounds BIB003 . Eventually, novel algorithms and auditory computational models will be produced to improve the performance of machines in sound localization and audio understanding in complicated auditory environments.