“…An increasing number of studies has demonstrated that neural representations of acoustic (Park et al, 2015; Park et al, 2016; Hauswald et al, 2018; Park et al, 2018b; Park et al, 2018a; Biau et al, 2021; Haider et al, 2022) and linguistic, e.g., semantic features (Kutas and Federmeier, 2011; Strauss et al, 2014; Huth et al, 2016; Wang et al, 2018; Broderick et al, 2019; Kaufeld et al, 2020) of naturalistic auditory or audiovisual speech are quantifiable in Magnetoencephalography (MEG) or Electroencephalography (EEG) recordings based on frequency-domain synchronization analysis or time-domain regression analysis. Furthermore, recent developments of natural language processing (NLP) models based on machine learning algorithms, such as word vectors (Mikolov et al, 2013), have brought breakthroughs not only to the area of artificial intelligence (AI), for example, speech/text recognition, machine translations (e.g., speech-to-text), but also to the neuroscientific study of rich, naturalistic speech stimuli (Broderick et al, 2018; Pereira et al, 2018) or movie (Nishida et al, 2021).…”