Effects of intelligibility on within- and cross-modal sentence recognition memory for native and non-native listeners

Abstract

The goal of the study was to examine whether enhancing the clarity of the speech signal through conversational-to-clear speech modifications improves sentence recognition memory for native and non-native listeners, and if so, whether this effect would hold when the stimuli in the test phase are presented in orthographic instead of auditory form (cross-modal presentation). Sixty listeners (30 native and 30 non-native English) participated in a within-modal (i.e., audio-audio) sentence recognition memory task (Experiment I). Sixty different individuals (30 native and 30 non-native English) participated in a cross-modal (i.e., audio-textual) sentence recognition memory task (Experiment II). The results showed that listener-oriented clear speech enhanced sentence recognition memory for both listener groups regardless of whether the acoustic signal was present during the test phase (Experiment I) or absent (Experiment II). Compared to native listeners, non-native listeners had longer reaction times in the within-modal task and were overall less accurate in the cross-modal task. The results showed that more cognitive resources remained available for storing information in memory during processing of easier-to-understand clearly produced sentences. Furthermore, non-native listeners benefited from signal clarity in sentence recognition memory despite processing speech signals in a cognitively more demanding second language.

Face masks and speaking style affect audio-visual word recognition and memory of native and non-native speech

“…That is, if the listeners could not hear the sentences, they would not be able to recall the information. Previous work, though, showed that even highly intelligible conversational sentences were recalled less well compared to clear sentences, suggesting an increased processing cost that is independent of accurate word recognition ( Keerstock and Smiljanic, 2018 , 2019 ; Winn and Teece, 2020 ). Similarly, greater cognitive load indicated by greater pupil dilation was found for an L2 talker compared to an L1 talker even when recognition accuracy was at ceiling for both talkers, as was found here in the quiet condition ( McLaughlin and Van Engen, 2020 ).…”

“…Clear speech benefit extends to speech processing beyond word recognition; it improves recognition memory and recall for speech in quiet and in noise ( Gilbert et al , 2014 ; Keerstock and Smiljanic, 2018 , 2019 ; Van Engen et al , 2012 ). The exaggerated acoustic-phonetic clear speech cues seem to enhance memory traces for sentences produced in that style, enabling listeners to retain more information.…”

The role of speech style, frequency, and density in recognition memory for spoken words

“…In a follow-up study, Keerstock and Smiljanić (2019) conducted a cued-recall experiment, which is more difficult than a yes/no recognition memory task. Results showed that more words were recalled in the clear speech condition; as in Keerstock and Smiljanić (2018) , this effect occurred for both L1 and L2 listening populations.…”

Signal Recognition for English Speech Translation Based on Improved Wavelet Denoising Method

“…The corresponding scoring function includes the likelihood score of the signal, the score of the sentence, and the score of the posterior probability of the data. The corresponding scoring formulas are shown in formula (13), formula (14), and formula (15), respectively. Final output voice results…”

“…The wavelet transform denoising algorithm is essentially a multiscale signal analysis algorithm, which can map the speech signal to be processed to the corresponding wavelet domain, and carry out targeted processing on the noisy wavelet coefficients that do not pass the speech characteristics in different sizes according to the corresponding speech system and the wavelet coefficients of noise [10]. In the actual processing process, based on the series space function obtained by the expansion and translation of the wavelet generating function corresponding to the wavelet signal, and according to the corresponding threshold standard, the best approximation of the original signal is found, so as to distinguish the original signal from the noise signal [11][12][13][14]. However, the traditional wavelet signal is subject to the selection of threshold.…”