Different Levels Of Background Noise Research Articles

Selective Word Stress Effect for Speech Perception Enhancement in Hearing-Impaired Older Listeners

Purpose: Although it is known that communication problem of the older adults might be overcome by using slower speech or talking in a less noisy environment, there is still a lack of detailed skills. This study aimed to identify word stress benefit in speech perception for older listeners when different levels of background noise and speech rate were presented.Methods: Forty elderly (20 for hearing loss and 20 for normal hearing) were randomly recruited. Both groups conducted sentence tests at quiet and three signal-to-noise ratio (SNR) (+6, +3, 0 dB) and did them under four timealteration conditions (± 30% and ± 15%). Further, all sentences of both the noise and time alteration conditions had a 4, 6, or 8 dB up emphasized key word.Results: As expected, the hearing-impaired group had lower scores than the group with normal hearing in quiet and three SNR conditions. In quiet, both groups performed best at 6 dB emphasis. However, there was no significant difference in performance across 4, 6, and 8 dB up stress conditions when background noise was present. As time compression increased, speech perception scores decreased in both groups. Notably, at 30% expansion (slower speed), the normal hearing group scored the highest at 4 dB up emphasis, while the hearing-impaired group showed the best performance best at 8 dB up emphasis.Conclusion: Based on the current results, rather than simply speaking slowly, it would be more helpful for older adults with hearing loss to control their speech rate also emphasize key words during the conversation.

One, Two, Three! Can Everybody Hear Me? Acoustics of Roman Contiones. Case Studies of the Capitoline Hill and the Temple of Bellona in Rome

Abstract Rhetoric was one of the cornerstones of Roman education and public speaking, the essence of being a Roman politician. The speakers attempted to captivate the audience with their style and convince them of their arguments. Studying the audience is therefore just as important as investigating the speakers and their speeches. The aim of this article is to estimate the number of people who could intelligibly hear a speaker delivering a speech from two speaking platforms located in the city of Rome: the podium of the Temple of Bellona in the Campus Martius (in the Late Republican and Late Augustan periods) and the Capitoline Temple. To do this, we built virtual reconstructions of both venues according to the current state of knowledge about them, taking into account the geometry of the space as well as the materials from which they were built. On the models thus prepared, we carried out acoustic simulations for three different levels of background noise (36, 49, and 55 dBA), resulting in Speech Transmission Index maps. The results became the basis upon which we estimated the size of the maximum potential crowds that could hear speech intelligibly, using two methods based on the behaviour of contemporary crowds. We further compared our results with those of previous studies that concern other speaking platforms in Rome.

Music in noise recognition: An EEG study of listening effort in cochlear implant users and normal hearing controls.

Despite the plethora of studies investigating listening effort and the amount of research concerning music perception by cochlear implant (CI) users, the investigation of the influence of background noise on music processing has never been performed. Given the typical speech in noise recognition task for the listening effort assessment, the aim of the present study was to investigate the listening effort during an emotional categorization task on musical pieces with different levels of background noise. The listening effort was investigated, in addition to participants' ratings and performances, using EEG features known to be involved in such phenomenon, that is alpha activity in parietal areas and in the left inferior frontal gyrus (IFG), that includes the Broca's area. Results showed that CI users performed worse than normal hearing (NH) controls in the recognition of the emotional content of the stimuli. Furthermore, when considering the alpha activity corresponding to the listening to signal to noise ratio (SNR) 5 and SNR10 conditions subtracted of the activity while listening to the Quiet condition-ideally removing the emotional content of the music and isolating the difficulty level due to the SNRs- CI users reported higher levels of activity in the parietal alpha and in the homologous of the left IFG in the right hemisphere (F8 EEG channel), in comparison to NH. Finally, a novel suggestion of a particular sensitivity of F8 for SNR-related listening effort in music was provided.

Different housing conditions for zebrafish: What are the effects?

Zebrafish is a popular experimental model in several research areas but little is known about the effects of using different strains or housing conditions. Poor control of genetic background and housing conditions could affect experimental results and data reproducibility. Here we investigated the effects of two possible sources of variation on zebrafish behaviour: fish origin and environmental parameters (light intensity, water temperature and noise). Zebrafish behaviour was then examined using the ‘novel tank test’, one of the most common paradigms used to assess anxiety-like behaviours in zebrafish. Our results show that an increase in light intensity alters fish behaviour, particularly freezing duration and distance from the bottom of the tank, indicating increased anxiety. Swimming activity increased at the lowest temperature (25 °C). However, different levels of background noise did not cause any significant changes in behaviour. Differences were also found between zebrafish strains and populations: while the AB strain from laboratory 1 was minimally influenced by variation in holding conditions, the AB strain from laboratory 2 was highly affected by changes in temperature, light, and background noise. Our study shows that variation in strains and holding conditions can significantly influence the results of behavioural testing and should be carefully considered in the experimental design and properly reported to improve data interpretation and reproducibility.

Variación de los parámetros acústicos: f0, Jitter, Shimmer y Alpha ratio en relación con distintos niveles de ruido de fondo

Background and objectivesResearch involving acoustic analysis does not always consider the level of background noise in the room where the recording process is going to be carried out, or if they do, the maximum value of background noise used varies from author to author. The objective of this research is to establish if the parameters: f0, Jitter, Shimmer and Alpha ratio vary in relation to different levels of background noise in the room where the recording process is carried out and to establish a maximum value of this. Materials and methodsThe Praat program was used to get the average value of the parameters: f0, Jitter, Shimmer and Alpha ratio from a voice sample of 33 subjects inside an audiometric booth whose background noise level was 28.1dB(A). A statistical comparison was made between the average value of each parameter as the background noise increased in relation to the average values obtained with the baseline background noise of the cabin for each parameter. ResultsThe f0 and Alpha ratio parameters show reliable values up to a background noise level of 47.7dB(A), while the Jitter and Shimmer parameters do so up to a background noise level of 43.8dB(A). ConclusionsConsidering that the acoustic parameters are all obtained from the same recording, we can point out that the recommended maximum background noise level in the room where the recordings are made should not exceed 43.8dB(A).

Variation of the acoustic parameters: f0, jitter, shimmer and alpha ratio in relation with different background noise levels

Background and objectivesResearch involving acoustic analysis does not always consider the level of background noise in the room where the recording process is going to be carried out, or if they do, the maximum value of background noise used varies from author to author. The objective of this research is to establish if the parameters: f0, Jitter, Shimmer and Alpha ratio vary in relation to different levels of background noise in the room where the recording process is carried out and to establish a maximum value of this. Materials and methodsThe Praat program was used to get the average value of the parameters: f0, Jitter, Shimmer and Alpha ratio from a voice sample of 33 subjects inside an Audiometric booth whose background noise level was 28,1 dB(A). A statistical comparison was made between the average value of each parameter as the background noise increased in relation to the average values ​​obtained with the baseline background noise of the cabin for each parameter. ResultsThe f0 and Alpha ratio parameters show reliable values ​​up to a background noise level of 47,7 dB(A), while the Jitter and Shimmer parameters do so up to a background noise level of 43,8 dB(A). ConclusionsConsidering that the acoustic parameters are all obtained from the same recording, we can point out that the recommended maximum background noise level in the room where the recordings are made should not exceed 43,8 dB(A).

Variation of the Acoustic Parameter Harmonic-to-noise Ratio in Relation to Different Background Noise Levels

Introduction and objectivesAcoustic analysis is a tool widely used by professionals related to the study of voice that gives us information from a recording. It has been identified that in investigations carried out today that involve acoustic analysis, the recording process takes place in rooms with different levels of background noise. The objective of this study is to establish whether the acoustic parameter, Harmonic to Noise Ratio (HNR) varies in relation to the different background noise levels of the premises where the recording is made and recommend a maximum level of background noise. MethodsThrough the Praat programme, the average value of the acoustic parameter HNR of 43 subjects was obtained, inside the audiometric booth of the Universidad Autónoma de Chile, whose background noise level is 28.1 dB (A) and it was compared with the HNR average values obtained with higher background noise levels. ResultsThe HNR average values decreased from 19.8 dB (A) to 14.0 dB (A) as the background noise level of the premises increased from 28.1 dB (A) to 57.8 dB (A). The t-test for dependent samples was performed, with which the HNR average values obtained were compared with the baseline background noise level (28.1 dB (A)). Significant differences were found with the baseline background noise level when the room noise level was higher than 47.7 dB (A). ConclusionsThe background noise of the premises where the voice recording for the acoustic analysis is performed, influences the value of the acoustic parameter HNR, which decreases as the background noise of the premises increases. It is recommended that the background noise level should not exceed 43.8 dB (A).

Comparing Emotion Recognition and Word Recognition in Background Noise.

Purpose Word recognition in quiet and in background noise has been thoroughly investigated in previous research to establish segmental speech recognition performance as a function of stimulus characteristics (e.g., audibility). Similar methods to investigate recognition performance for suprasegmental information (e.g., acoustic cues used to make judgments of talker age, sex, or emotional state) have not been performed. In this work, we directly compared emotion and word recognition performance in different levels of background noise to identify psychoacoustic properties of emotion recognition (globally and for specific emotion categories) relative to word recognition. Method Twenty young adult listeners with normal hearing listened to sentences and either reported a target word in each sentence or selected the emotion of the talker from a list of options (angry, calm, happy, and sad) at four signal-to-noise ratios in a background of white noise. Psychometric functions were fit to the recognition data and used to estimate thresholds (midway points on the function) and slopes for word and emotion recognition. Results Thresholds for emotion recognition were approximately 10 dB better than word recognition thresholds, and slopes for emotion recognition were half of those measured for word recognition. Low-arousal emotions had poorer thresholds and shallower slopes than high-arousal emotions, suggesting greater confusion when distinguishing low-arousal emotional speech content. Conclusions Communication of a talker's emotional state continues to be perceptible to listeners in competitive listening environments, even after words are rendered inaudible. The arousal of emotional speech affects listeners' ability to discriminate between emotion categories.

Effect of Noise on Speech Intelligibility and Perceived Listening Effort in Head and Neck Cancer.

Purpose This study (a) examined the effect of different levels of background noise on speech intelligibility and perceived listening effort in speakers with impaired and intact speech following treatment for head and neck cancer (HNC) and (b) determined the relative contribution of speech intelligibility, speaker group, and background noise to a measure of perceived listening effort. Method Ten speakers diagnosed with nasal, oral, or oropharyngeal HNC provided audio recordings of six sentences from the Sentence Intelligibility Test. All speakers were 100% intelligible in quiet: Five speakers with HNC exhibited mild speech imprecisions (speech impairment group), and five speakers with HNC demonstrated intact speech (HNC control group). Speech recordings were presented to 30 inexperienced listeners, who transcribed the sentences and rated perceived listening effort in quiet and two levels (+7 and +5 dB SNR) of background noise. Results Significant Group × Noise interactions were found for speech intelligibility and perceived listening effort. While no differences in speech intelligibility were found between the speaker groups in quiet, the results showed that, as the signal-to-noise ratio decreased, speakers with intact speech (HNC control) performed significantly better (greater intelligibility, less perceived listening effort) than those with speech imprecisions in the two noise conditions. Perceived listening effort was also shown to be associated with decreased speech intelligibility, imprecise speech, and increased background noise. Conclusions Speakers with HNC who are 100% intelligible in quiet but who exhibit some degree of imprecise speech are particularly vulnerable to the effects of increased background noise in comparison to those with intact speech. Results have implications for speech evaluations, counseling, and rehabilitation.

Effects of face masks on speech recognition in multi-talker babble noise

Face masks are an important tool for preventing the spread of COVID-19. However, it is unclear how different types of masks affect speech recognition in different levels of background noise. To address this, we investigated the effects of four masks (a surgical mask, N95 respirator, and two cloth masks) on recognition of spoken sentences in multi-talker babble. In low levels of background noise, masks had little to no effect, with no more than a 5.5% decrease in mean accuracy compared to a no-mask condition. In high levels of noise, mean accuracy was 2.8-18.2% lower than the no-mask condition, but the surgical mask continued to show no significant difference. The results demonstrate that different types of masks generally yield similar accuracy in low levels of background noise, but differences between masks become more apparent in high levels of noise.

SCAN-ATAC-Sim: a scalable and efficient method for simulating single-cell ATAC-seq data from bulk-tissue experiments.

scATAC-seq is a powerful approach for characterizing cell-type-specific regulatory landscapes. However, it is difficult to benchmark the performance of various scATAC-seq analysis techniques (such as clustering and deconvolution) without having a priori a known set of gold-standard cell types. To simulate scATAC-seq experiments with known cell-type labels, we introduce an efficient and scalable scATAC-seq simulation method (SCAN-ATAC-Sim) that down-samples bulk ATAC-seq data (e.g. from representative cell lines or tissues). Our protocol uses a consistent but tunable signal-to-noise ratio across cell types in a scATAC-seq simulation for integrating bulk experiments with different levels of background noise, and it independently samples twice without replacement to account for the diploid genome. Because it uses an efficient weighted reservoir sampling algorithm and is highly parallelizable with OpenMP, our implementation in C++ allows millions of cells to be simulated in less than an hour on a laptop computer. SCAN-ATAC-Sim is available at scan-atac-sim.gersteinlab.org. Supplementary data are available at Bioinformatics online.

Variación del parámetro acústico harmonic-to-noise ratio en relación con distintos niveles de ruido de fondo

Introduction and objectivesAcoustic analysis is a tool widely used by professionals related to the study of voice that gives us information from a recording. It has been identified that in investigations carried out today that involve acoustic analysis, the recording process takes place in rooms with different levels of background noise. The objective of this study is to establish whether the acoustic parameter harmonic-to-noise ratio (HNR) varies in relation to the different background noise levels of the premises where the recording is made and recommend a maximum level of background noise. MethodsThrough the Praat programme, the average value of the acoustic parameter HNR of 43 subjects was obtained, inside the audiometric booth of the Universidad Autónoma de Chile, whose background noise level is 28.1dB(A) and it was compared with the HNR average values obtained with higher background noise levels. ResultsThe HNR average values decreased from 19.8 to 14.0dB(A) as the background noise level of the premises increased from 28.1 to 57.8dB(A). The t-test for dependent samples was performed, with which the HNR average values obtained were compared with the baseline background noise level (28.1dB[A]). Significant differences were found with the baseline background noise level when the room noise level was higher than 47.7dB(A). ConclusionsThe background noise of the premises where the voice recording for the acoustic analysis is performed, influences the value of the acoustic parameter HNR, which decreases as the background noise of the premises increases. It is recommended that the background noise level should not exceed 43.8dB(A).

Integration of Remote Sensing and GIS to Extract Plantation Rows from A Drone-Based Image Point Cloud Digital Surface Model

Automated feature extraction from drone-based image point clouds (DIPC) is of paramount importance in precision agriculture (PA). PA is blessed with mechanized row seedlings to attain maximum yield and best management practices. Therefore, automated plantation rows extraction is essential in crop harvesting, pest management, and plant grow-rate predictions. Most of the existing research is consists on red, green, and blue (RGB) image-based solutions to extract plantation rows with the minimal background noise of test study sites. DIPC-based DSM row extraction solutions have not been tested frequently. In this research work, an automated method is designed to extract plantation row from DIPC-based DSM. The chosen plantation compartments have three different levels of background noise in UAVs images, therefore, methodology was tested under different background noises. The extraction results were quantified in terms of completeness, correctness, quality, and F1-score values. The case study revealed the potential of DIPC-based solution to extraction the plantation rows with an F1-score value of 0.94 for a plantation compartment with minimal background noises, 0.91 value for a highly noised compartment, and 0.85 for a compartment where DIPC was compromised. The evaluation suggests that DSM-based solutions are robust as compared to RGB image-based solutions to extract plantation-rows. Additionally, DSM-based solutions can be further extended to assess the plantation rows surface deformation caused by humans and machines and state-of-the-art is redefined.

EchoFace: Acoustic Sensor-Based Media Attack Detection for Face Authentication

Face authentication systems have gained widespread popularity because of their user-friendly usage and increasing recognition accuracy. Unfortunately, the boom in mobile social networks has bought with it media-based facial forgery; a critical threat where an adversary forges or replays the victim’s photograph/video to fool the system. In this article, we propose EchoFace, an effective and robust liveness detection system to enhance face authentication in defending against media-based attacks, which works with today’s smartphones/smartwatches without any hardware modification. EchoFace uses active acoustic sensing to differentiate the uneven stereostructure of the face and the flat forged media. Our proposed scheme effectively extracts the desired reflection profiles from the target. Moreover, we propose effective similarity measurements of reflection profiles to distinguish live users from forged media, which works robustly under various environmental conditions. EchoFace only requires low cost and universally equipped acoustic sensors without human intervention for liveness detection, which can be easily deployed in a variety of application scenarios. We implement EchoFace on commercial smartphones, and experiment results show that EchoFace achieves an average detection accuracy higher than 96% and false alarm rate lower than 4% across various media attacks and different levels of background noise. This shows its great potential to enhance the security of widely deployed face authentication systems in real scenarios.

Measuring the H i mass function below the detection threshold

Abstract We present a Bayesian stacking technique to directly measure the H i mass function (HIMF) and its evolution with redshift using galaxies formally below the nominal detection threshold. We generate galaxy samples over several sky areas given an assumed HIMF described by a Schechter function and simulate the H i emission lines with different levels of background noise to test the technique. We use Multinest to constrain the parameters of the HIMF in a broad redshift bin, demonstrating that the HIMF can be accurately reconstructed, using the simulated spectral cube far below the H i mass limit determined by the 5σ flux-density limit, i.e. down to MHI = 107.5 M⊙ over the redshift range 0 < z < 0.55 for this particular simulation, with a noise level similar to that expected for the MIGHTEE survey. We also find that the constraints on the parameters of the Schechter function, φ⋆, M⋆ and α can be reliably fit, becoming tighter as the background noise decreases as expected, although the constraints on the redshift evolution are not significantly affected. All the parameters become better constrained as the survey area increases. In summary, we provide an optimal method for estimating the H i mass at cosmological distances that allows us to constrain the H i mass function below the detection threshold in forthcoming H i surveys. This study is a first step towards the measurement of the HIMF at high (z > 0.1) redshifts.

Speech, movement, and gaze behaviours during dyadic conversation in noise

How do people have conversations in noise and make themselves understood? While many previous studies have investigated speaking and listening in isolation, this study focuses on the behaviour of pairs of individuals in an ecologically valid context. Specifically, we report the fine-grained dynamics of natural conversation between interlocutors of varying hearing ability (n = 30), addressing how different levels of background noise affect speech, movement, and gaze behaviours. We found that as noise increased, people spoke louder and moved closer together, although these behaviours provided relatively small acoustic benefit (0.32 dB speech level increase per 1 dB noise increase). We also found that increased noise led to shorter utterances and increased gaze to the speaker’s mouth. Surprisingly, interlocutors did not make use of potentially beneficial head orientations. While participants were able to sustain conversation in noise of up to 72 dB, changes in conversation structure suggested increased difficulty at 78 dB, with a significant decrease in turn-taking success. Understanding these natural conversation behaviours could inform broader models of interpersonal communication, and be applied to the development of new communication technologies. Furthermore, comparing these findings with those from isolation paradigms demonstrates the importance of investigating social processes in ecologically valid multi-person situations.

Neural Speech Tracking in the Theta and in the Delta Frequency Band Differentially Encode Clarity and Comprehension of Speech in Noise.

Humans excel at understanding speech even in adverse conditions such as background noise. Speech processing may be aided by cortical activity in the delta and theta frequency bands, which have been found to track the speech envelope. However, the rhythm of non-speech sounds is tracked by cortical activity as well. It therefore remains unclear which aspects of neural speech tracking represent the processing of acoustic features, related to the clarity of speech, and which aspects reflect higher-level linguistic processing related to speech comprehension. Here we disambiguate the roles of cortical tracking for speech clarity and comprehension through recording EEG responses to native and foreign language in different levels of background noise, for which clarity and comprehension vary independently. We then use a both a decoding and an encoding approach to relate clarity and comprehension to the neural responses. We find that cortical tracking in the theta frequency band is mainly correlated to clarity, whereas the delta band contributes most to speech comprehension. Moreover, we uncover an early neural component in the delta band that informs on comprehension and that may reflect a predictive mechanism for language processing. Our results disentangle the functional contributions of cortical speech tracking in the delta and theta bands to speech processing. They also show that both speech clarity and comprehension can be accurately decoded from relatively short segments of EEG recordings, which may have applications in future mind-controlled auditory prosthesis.SIGNIFICANCE STATEMENT Speech is a highly complex signal whose processing requires analysis from lower-level acoustic features to higher-level linguistic information. Recent work has shown that neural activity in the delta and theta frequency bands track the rhythm of speech, but the role of this tracking for speech processing remains unclear. Here we disentangle the roles of cortical entrainment in different frequency bands and at different temporal lags for speech clarity, reflecting the acoustics of the signal, and speech comprehension, related to linguistic processing. We show that cortical speech tracking in the theta frequency band encodes mostly speech clarity, and thus acoustic aspects of the signal, whereas speech tracking in the delta band encodes the higher-level speech comprehension.

Structure of information technology for the removal of emotional signs from a speech signal

The goal of the article is to describe the methods and algorithms of information technology for removal of emotional signs from the speech signal which has been proposed by the authors. Methods of extracting and transforming emotional features, as well as methods of classification of speech signals obtained after transformation are described. The task of removing emotions will be considered successful if, after the transformations, the emotion classifier assigns a speech signal to a class of emotionless speech. The scheme for solving the problem consists of the following steps: 1) extracting informative features and establishing the boundaries of conversion; 2) transforming input data informative features; 3) checking the effectiveness of the transformation. The first algorithm used for transformation is the PSOLA (Pitch-Synchronous OverLap and Add) algorithm designed to change the pitch of the main tone by stretching and compressing the signal on the timeline. The PSOLA algorithm can be used to shift the pitch of voice, retaining the formant position, and thus the identity of the vowels. The basic idea is to extend the time on pitch marks, while the shape of the audio wave of the segment does not change. The second method of transformation, the normalization of the dynamic range of the logarithm of energy, reduces the distribution of energy due to different levels of background noise [2]. The input parameter for the energy normalization unit is the frame energy. Energy is replaced by the energy logarithm based on the similarity to the logarithmic perception of sound by the human auditory system, and also due to the stability of the logarithm to the sharp changes in the energy level. To test the effectiveness of the transformation, the task of recognizing emotions in a language requires the building of a classifier. The input data for classifying and recognizing were emotional expressions, each of which was represented by a set of signs: the frequency of the main tone, the energy and the frequency of crossing zero. Global signs of the minimum, the maximum and the average of each of the sets of values are the vector of expressions. For the classification a model of a mixture of normal sections was used. Using the constructed statistical model of the speech signal, the a posteriori probability of belonging of the utterance to the emotionally neutral class was calculated. The output speech signal meets the set criterion of a successful solution of the problem as formal (result of classification) and perceptual signs, which allows to consider the task of removing emotional features solved.

Tracking eye and head movements in natural conversational settings: Effects of hearing loss and background noise level

Although beam-forming algorithms for hearing aids can produce gains in target-to-masker, the wearer’s head will not always be facing the target talker, potentially limiting the value of beam-forming in real-world environments, unless eye movements are also accounted for. The aim of this study was to determine the extent to which the head direction and eye gaze track the position of the talker in natural conversational settings. Three groups of participants were recruited: younger listeners, older listeners with clinically normal hearing, and older listeners with mild-to-moderate hearing loss. The experimental set-up included one participant at a time in conversation with two confederates approximately equally spaced around a small round table. Different levels of background noise were introduced by playing background sounds via loudspeakers that surrounded the participants in the conversation. In general, head movements tended to undershoot the position of the current talker, but head and eye movements together generally predicted the current talker position well. Preliminary data revealed no strong effects of hearing loss, or background noise level on the amount of time spent looking at the talker, although younger listeners tended to use their eyes, as opposed to head movements, more than the older listeners. [Work supported by Starkey Laboratories.]

Cortical oscillations and entrainment in speech processing during working memory load.

Neuronal oscillations are thought to play an important role in working memory (WM) and speech processing. Listening to speech in real‐life situations is often cognitively demanding but it is unknown whether WM load influences how auditory cortical activity synchronizes to speech features. Here, we developed an auditory n‐back paradigm to investigate cortical entrainment to speech envelope fluctuations under different degrees of WM load. We measured the electroencephalogram, pupil dilations and behavioural performance from 22 subjects listening to continuous speech with an embedded n‐back task. The speech stimuli consisted of long spoken number sequences created to match natural speech in terms of sentence intonation, syllabic rate and phonetic content. To burden different WM functions during speech processing, listeners performed an n‐back task on the speech sequences in different levels of background noise. Increasing WM load at higher n‐back levels was associated with a decrease in posterior alpha power as well as increased pupil dilations. Frontal theta power increased at the start of the trial and increased additionally with higher n‐back level. The observed alpha–theta power changes are consistent with visual n‐back paradigms suggesting general oscillatory correlates of WM processing load. Speech entrainment was measured as a linear mapping between the envelope of the speech signal and low‐frequency cortical activity (< 13 Hz). We found that increases in both types of WM load (background noise and n‐back level) decreased cortical speech envelope entrainment. Although entrainment persisted under high load, our results suggest a top‐down influence of WM processing on cortical speech entrainment.