A comparison of monolingual and bilingual toddlers’ word recognition in noise

2021 ◽  
pp. 136700692110286
Author(s):  
Giovanna Morini ◽  
Rochelle S. Newman
Keyword(s):  
Background Noise ◽  
Word Identification ◽  
Bilingual Children ◽  
Noise Levels ◽  
High Noise ◽  
Speech In Noise ◽  
Mixed Analysis ◽  
Correct Object ◽  
Language Exposure ◽  
And Performance

Aims and objectives: The purpose of this study was to examine whether differences in language exposure (i.e., being raised in a bilingual versus a monolingual environment) influence young children’s ability to comprehend words when speech is heard in the presence of background noise. Methodology: Forty-four children (22 monolinguals and 22 bilinguals) between the ages of 29 and 31 months completed a preferential looking task where they saw picture-pairs of familiar objects (e.g., balloon and apple) on a screen and simultaneously heard sentences instructing them to locate one of the objects (e.g., look at the apple!). Speech was heard in quiet and in the presence of competing white noise. Data and analyses: Children’s eye-movements were coded off-line to identify the proportion of time they fixated on the correct object on the screen and performance across groups was compared using a 2 × 3 mixed analysis of variance. Findings: Bilingual toddlers performed worse than monolinguals during the task. This group difference in performance was particularly clear when the listening condition contained background noise. Originality: There are clear differences in how infants and adults process speech in noise. To date, developmental work on this topic has mainly been carried out with monolingual infants. This study is one of the first to examine how background noise might influence word identification in young bilingual children who are just starting to acquire their languages. Significance: High noise levels are often reported in daycares and classrooms where bilingual children are present. Therefore, this work has important implications for learning and education practices with young bilinguals.

Noise Control Program for a Large Tractor Tug

1998 ◽  
Vol 35 (01) ◽  
pp. 55-64
Author(s):  
Raymond W. Fischer ◽  
Warren Snider ◽  
William L. Hurley
Keyword(s):  
Diesel Exhaust ◽  
Vibration Isolation ◽  
Noise Control ◽  
Prior Experience ◽  
Control Program ◽  
Noise Levels ◽  
Control Effort ◽  
High Noise ◽  
And Performance ◽  
Design Construction

To effectively reduce noise on small, high powered vessels such as tugboats, it is necessary to conduct a concerted noise control effort. For optimal improvement, the program must be carried through all phases of the design, construction and testing. This paper describes a program instituted by the owner and design agent early in the procurement process to control habitability noise on a large tractor tug. The owners, dedicated to maximizing crew comfort and performance while recognizing the benefits of on-deck communications, have found from prior experience that without appropriate treatments the tug would have unacceptably high noise levels. Noise goals were established and treatments were selected, both in terms of acoustic and non-acoustic impacts. Detail designs were developed for floating room treatments, genset and diesel exhaust vibration isolation, and absorptive materials. Later tests revealed that the noise goals had been met. This paper describes the program from initiation through acoustic trials, provides insights into some problem areas and makes recommendations for future designs.

scholarly journals Perception of concatenative vs. neural text-to-speech (TTS): Differences in intelligibility in noise and language attitudes

2020 ◽  
Author(s):  
Michelle Cohn ◽  
Georgia Zellou
Keyword(s):  
Speech Intelligibility ◽  
Language Attitudes ◽  
Word Identification ◽  
Social Judgments ◽  
Text To Speech ◽  
Noise Levels ◽  
Correct Word ◽  
Speech Technology ◽  
Speech In Noise ◽  
Accuracy Measures

In this study, we test two questions of how users perceive neural vs. concatenative text-to-speech (TTS): 1) does the TTS method influence speech intelligibility in adverse listening conditions? and 2) does a user’s ratings of the voice’s social attributes shape intelligibility? We used identical speaker training datasets for a set of 4 speakers (using AWS Polly TTS). In Experiment 1, listeners identified target words in semantically predictable and unpredictable sentences generated in concatenative and neural TTS at two noise levels (-3 dB, -6 dB SNR). Correct word identification was lower for neural TTS than concatenative TTS, in the lower SNR, and for semantically unpredictable sentences. In Experiment 2, listeners rated the voices on 4 social attributes: sentences generated with neural TTS were rated as more human-like, natural, likeable, and familiar than concatenative TTS utterances. Furthermore, we observed individual variation in a given listener’s SPIN accuracy measures: how human- like/natural they rated the neural TTS voice was positively related to their speech-in-noise accuracy. Together, these findings show that the TTS method influences both intelligibility and social judgments of speech —and that these factors are linked. Overall, this work contributes to our understanding of the nexus of speech technology and human speech perception.

Effects of continuous noise backgrounds on rate response of auditory nerve fibers in cat

1984 ◽  
Vol 51 (6) ◽  
pp. 1326-1344 ◽  
Author(s):  
J. A. Costalupes ◽  
E. D. Young ◽  
D. J. Gibson
Keyword(s):  
Auditory Nerve ◽  
Noise Level ◽  
Background Noise ◽  
Dynamic Range ◽  
Discharge Rate ◽  
Nerve Fibers ◽  
Noise Levels ◽  
High Noise ◽  
Continuous Noise ◽  
Auditory Nerve Fibers

This study describes the effects of broadband background noise on the average discharge rate to best-frequency (BF) tones of auditory nerve fibers in the cat. The effects of exposure to long-term continuous noise are compared to the effects of noise gated on and off simultaneously with test tones. Addition of background noise causes a shift of the dynamic portion of tone-evoked rate versus level functions to higher tone intensities. The shift occurs at a mean rate of 0.61 dB of shift for each 1-dB increment in noise level. The rate of shift is independent of best frequency and spontaneous discharge rate. The noise level at which the shift begins is frequency dependent and is consistent with the frequency-dependent bandwidths of auditory nerve fiber tuning curves. The adjustment of the dynamic range shows many similarities to two-tone suppression. Therefore, it is most likely that it is caused by suppression of the response to the BF test tone by energy present in the noise at surrounding frequencies. At high noise levels, the ability of auditory nerve fibers to respond to test tones is limited by the rate response to the noise. As noise level increases, the discharge rate it evokes approaches a fiber's saturation rate and ultimately eliminates the fiber's ability to respond to test tones. Low spontaneous rate fibers, which have been shown to have higher thresholds and wider dynamic range (17,29), are significantly more resistant to saturation by high noise levels. Exposure to broadband noise prior to onset of test tones produces an overall decrement in response rate. This phenomenon is similar to the effects of short-term adaptation (32) and seems to develop independently of the shift of dynamic range. At high noise levels, previous exposure to the noise produces a small dynamic range shift. This effect is similar to that produced by suppression but is smaller. The effect is occluded in continuous noise backgrounds by the adjustment of sensitivity produced by suppression.

Effects of Classroom Noise on Students and Teachers in Greece

2003 ◽  
Vol 96 (2) ◽  
pp. 539-544 ◽  
Author(s):  
Dimitris Skarlatos ◽  
Manolis Manatakis
Keyword(s):  
High School ◽  
Background Noise ◽  
Reverberation Time ◽  
Noise Levels ◽  
High Background ◽  
High Noise ◽  
Significant Percentage ◽  
Time Period ◽  
High Background Noise

Measurements inside 32 occupied high-school classrooms during the courses showed that the measured long-term equivalent noise levels are high. A significant percentage of students and teachers found these levels unacceptable. The observed noise levels depend on the time period of the course, the age, and the number of the students in each classroom. The large reverberation time and the high background noise are responsible for the measured high noise level.

scholarly journals An Assessment of the Impact of Helicopter Noise: Case Study of Mgbuoshimini Community Nigeria

2020 ◽  
pp. 1-16
Author(s):  
OF Orikpete ◽  
◽  
DRE Ewim ◽  
Keyword(s):  
Background Noise ◽  
Low Noise ◽  
Sound Level ◽  
Residential Areas ◽  
Noise Levels ◽  
Commercial Aviation ◽  
High Noise ◽  
Statistical Measures ◽  
Level Meter ◽  
The Impact

With the projected growth in demand for commercial aviation, it is anticipated that there will be an increased environmental impact associated with noise, air quality, and climate change. Against this backdrop, the noise levels experienced by the residents of Mgbuoshimini Community due to helicopters takeoff and landing were studied. The study was carried out for 14 days at three different locations using a Class 2 Optimus sound level meter from 7am to 5pm daily. Analysis involved the equivalent noise levels, statistical measures for the background noise, aircraft flyover noise as well as the Noise Gap Index (NGI) . It was found that the equivalent noise levels of the background noise and aircraft flyover noise range from 67.7 dBA to 72.4 dBA and 88.4 dBA to 88.6 dBA respectively. The peak background noise and aircraft flyover noise ranged from 69.17 dBA to 79 dBA and 93.2 dBA to 94.8 dBA respectively. These values exceeded the recommended value of 60 dBA for residential areas. Two models to determine the NGI for both low noise areas and high noise areas were developed. A correlation coefficient of 0.70 and 0.88 were obtained between the actual and predicted values of NGI for both low and high noise areas. Therefore, it is recommended that buildings should be adequately insulated by use of noise-absorbing materials. Furthermore, environmentally friendly (quieter) aircrafts should be used by the airline company operating in that area

The physical environment

2018 ◽  
Author(s):  
Philip James
Keyword(s):  
Physical Environment ◽  
Urban Climate ◽  
Urban Environments ◽  
Artificial Light ◽  
Noise Levels ◽  
Light At Night ◽  
High Noise ◽  
P Elements ◽  
Micro Organisms ◽  
Diversity Of Life

Elements of the physical aspects of urban environments determine which micro-organisms, plants, and animals live in urban environments. In this chapter, climate, air, water, soil, noise, and light are discussed. Urban environments are affected by the climate of the region in which they are located, and in turn and create their own, distinctive urban climate. Air, water, and soil are all affected by urbanization. Pollution of these elements is common. High noise levels and artificial light at night (ALAN—a new phenomenon) are both strongly associated with urban environments. Details of both are discussed. The discussion in this chapter provides a foundation for further exploration of the diversity of life in urban environments and for later exploration of how organisms adapt to urban living, which will be discussed in Parts II and III.

Improved Vancouver Raman Algorithm Based on Empirical Mode Decomposition for Denoising Biological Samples

2019 ◽  
Vol 73 (12) ◽  
pp. 1436-1450 ◽  
Author(s):  
Fabiola León-Bejarano ◽  
Martin O. Méndez ◽  
Miguel G. Ramírez-Elías ◽  
Alfonso Alba
Keyword(s):  
Empirical Mode Decomposition ◽  
Raman Spectra ◽  
Biological Samples ◽  
Biological Material ◽  
Noise Levels ◽  
Synthetic Material ◽  
High Noise ◽  
Mode Decomposition ◽  
Mean Filter ◽  
The Mean

A novel method based on the Vancouver Raman algorithm (VRA) and empirical mode decomposition (EMD) for denoising Raman spectra of biological samples is presented. The VRA is one of the most used methods for denoising Raman spectroscopy and is composed of two main steps: signal filtering and polynomial fitting. However, the signal filtering step consists in a simple mean filter that could eliminate spectrum peaks with small intensities or merge relatively close spectrum peaks into one single peak. Thus, the result is often sensitive to the order of the mean filter, so the user must choose it carefully to obtain the expected result; this introduces subjectivity in the process. To overcome these disadvantages, we propose a new algorithm, namely the modified-VRA (mVRA) with the following improvements: (1) to replace the mean filter step by EMD as an adaptive parameter-free signal processing method; and (2) to automate the selection of polynomial degree. The denoising capabilities of VRA, EMD, and mVRA were compared in Raman spectra of artificial data based on Teflon material, synthetic material obtained from vitamin E and paracetamol, and biological material of human nails and mouse brain. The correlation coefficient (ρ) was used to compare the performance of the methods. For the artificial Raman spectra, the denoised signal obtained by mVRA ([Formula: see text]) outperforms VRA ([Formula: see text]) for moderate to high noise levels whereas mVRA outperformed EMD ([Formula: see text]) for high noise levels. On the other hand, when it comes to modeling the underlying fluorescence signal of the samples (i.e., the baseline trend), the proposed method mVRA showed consistent results ([Formula: see text]. For Raman spectra of synthetic material, good performance of the three methods ([Formula: see text] for VRA, [Formula: see text] for EMD, and [Formula: see text] for mVRA) was obtained. Finally, in the biological material, mVRA and VRA showed similar results ([Formula: see text] for VRA, [Formula: see text] for EMD, and [Formula: see text] for mVRA); however, mVRA retains valuable information corresponding to relevant Raman peaks with small amplitude. Thus, the application of EMD as a filter in the VRA method provides a good alternative for denoising biological Raman spectra, since the information of the Raman peaks is conserved and parameter tuning is not required. Simultaneously, EMD allows the baseline correction to be automated.

The Canterbury Child's Aid: A Binaural Spatial Sensor for Research with Blind Children

1979 ◽  
Vol 73 (5) ◽  
pp. 179-184
Author(s):  
E. R. Strelow ◽  
J. T. Boys
Keyword(s):  
Future Development ◽  
Background Noise ◽  
Field Of View ◽  
Basic Operation ◽  
Noise Levels ◽  
Design Features ◽  
Blind Children ◽  
Automatic Level

A binaural sensory aid for research with blind children is described along with the rationale for its design features. The basic operation of this form of aid is described in detail with particular reference to those features that control the sensing parameters of range, direction and field of view. A novel automatic level controller which ensures that device sounds remain audible but not excessively loud, in spite of changes in background noise levels, is also described. The likely future development of these forms of sonic aids is discussed.

Noise Pollution in Onitsha Metropolis: Challenges and Solution

2021 ◽  
Vol 1 (2) ◽  
pp. 032-040
Author(s):  
Chris Onyeka Ekweozor ◽  
Johnbosco Emeka Umunnakwe ◽  
Leo O Osuji ◽  
Vincent C Weli
Keyword(s):  
State Government ◽  
Noise Level ◽  
Control Point ◽  
Noise Pollution ◽  
Health Impact ◽  
Dry Season ◽  
Noise Levels ◽  
Wet Season ◽  
High Noise ◽  
Anambra State

This study evaluated noise pollution in Onitsha metropolis, Anambra State, Nigeria in 2019. Noise levels were measured at forty sampling stations in the morning, afternoon and night within the study area for dry and wet seasons using modern noise level instruments. A control point was established at ldeani/Nnobi Junction with coordinates N 06o 05’.282’’ E 006o 55’.891’’ which was used as a reference point and for comparison with the sound levels recorded in designated locations. The results showed that the maximum noise level in the study area exceeded the Federal Ministry of Environment (FMEnv) limit by 7.8% in the dry season and by 13.11% in the wet season. Noise LAeq exceeded the NESREA LAeq limit by 29.89% in the dry season and by 33.44% in the wet season. The study indicated that the mean noise levels in the dry and wet seasons were within FMEnv limit of 90dB .It also showed that high noise levels were recorded around major junctions and market places within Onitsha, which are harmful to public health. The study further showed that transportation activities and trading activities at the market places are the main sources of high noise levels in the study area. Health impact assessment should be conducted in Onitsha metropolis for residents. State government should enforce compliance laws and regulate the activities of industries in the areas.

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