The Experts below are selected from a list of 327 Experts worldwide ranked by ideXlab platform

Rochelle S. Newman - One of the best experts on this subject based on the ideXlab platform.

  • The Cocktail Party Effect in the domestic dog (Canis familiaris)
    Animal Cognition, 2019
    Co-Authors: Amritha Mallikarjun, Emily Shroads, Rochelle S. Newman
    Abstract:

    Like humans, canine companions often find themselves in noisy environments, and are expected to respond to human speech despite potential distractors. Such environments pose particular problems for young children, who have limited linguistic knowledge. Here, we examined whether dogs show similar difficulties. We found that dogs prefer their name to a stress-matched foil in quiet conditions, despite hearing it spoken by a novel talker. They continued to prefer their name in the presence of multitalker human speech babble at signal-to-noise levels as low as 0 dB, when their name was the same intensity as the foil. This surpasses the performance of 1-year-old infants, who fail to prefer their name to a foil at 0 dB (Newman in Dev Psychol 41(2):352–362, 2005). Overall, we find better performance at name recognition in dogs that were trained to do tasks for humans, like service dogs, search-and-rescue dogs, and explosives detection dogs. These dogs were of several different breeds, and their tasks were widely different from one another. This suggests that their superior performance may be due to generally more training and better attention. In summary, these results demonstrate that dogs can recognize their name even in relatively difficult levels of multitalker babble, and that dogs who work with humans are especially adept at name recognition in comparison with companion dogs. Future studies will explore the Effect of different types of background noise on word recognition in dogs.

  • Infant’s perception of speech in noise: Effect of the number of background talkers
    Journal of the Acoustical Society of America, 2006
    Co-Authors: Rochelle S. Newman
    Abstract:

    A number of recent studies have investigated infants’ abilities to hear speech in noise [Newman & Jusczyk, ‘‘The Cocktail Party Effect in infants,’’ Percep. Psychophs. 58, 1145–1156 (1996); Newman, R. S., ‘‘The Cocktail Party Effect in infants revisited: Listening to one’s name in noise,’’ Develop. Psychol. 41, 352–362 (2005); Barker & Newman, ‘‘Listen to your mother! The role of talker familiarity in infant streaming,’’ Cognition, 94, B45–B53 (2004); Hollich, et al., ‘‘Infants’ use of synchronized visual information to seperate streams of speech,’’ Child Develop. 76, 598–613 (2005)]. In the current study, we examine how the type of background noise influences infants’ ability to understand speech. Infants aged 5 months heard a talker repeat either their own name or another infant’s name in the presence of one of 3 types of background noise: multitalker babble, a single background talker, or a single talker reversed in time. With multitalker babble, infants could recognize their name (shown by longer list...

  • the Cocktail Party Effect in infants revisited listening to one s name in noise
    Developmental Psychology, 2005
    Co-Authors: Rochelle S. Newman
    Abstract:

    : This study examined infants' abilities to separate speech from different talkers and to recognize a familiar word (the infant's own name) in the context of noise. In 4 experiments, infants heard repetitions of either their names or unfamiliar names in the presence of background babble. Five-month-old infants listened longer to their names when the target voice was 10 dB, but not 5 dB, more intense than the background. Nine-month-olds likewise failed to identify their names at a 5-dB signal-to-noise ratio, but 13-month-olds succeeded. Thus, by 5 months, infants possess some capacity to selectively attend to an interesting voice in the context of competing distractor voices. However, this ability is quite limited and develops further when infants near 1 year of age.

  • The Cocktail Party Effect in infants
    Perception & Psychophysics, 1996
    Co-Authors: Rochelle S. Newman, Peter W. Jusczyk
    Abstract:

    Most speech research with infants occurs in quiet laboratory rooms with no outside distractions. However, in the real world, speech directed to infants often occurs in the presence of other competing acoustic signals. To learn language, infants need to attend to their caregiver’s speech even under less than ideal listening conditions. We examined 7.5-month-old infants’ abilities to selectively attend to a female talker’s voice when a male voice was talking simultaneously. In three experiments, infants heard a target voice repeating isolated words while a distractor voice spoke fluently at one of three different intensities. Subsequently, infants heard passages produced by the target voice containing either the familiar words or novel words. Infants listened longer to the familiar words when the target voice was 10 dB or 5 dB more intense than the distractor, but not when the two voices were equally intense. In a fourth experiment, the assignment of words and passages to the familiarization and testing phases was reversed so that the passages and distractors were presented simultaneously during familiarization, and the infants were tested on the familiar and unfamiliar isolated words. During familiarization, the passages were 10 dB more intense than the distractors. The results suggest that this may be at the limits of what infants at this age can do in separating two different streams of speech. In conclusion, infants have some capacity to extract information from speech even in the face of a competing acoustic voice.

  • the Cocktail Party Effect infants use of visual information in speech segmentation
    1996
    Co-Authors: George Hollich, Peter W. Jusczyk, Rochelle S. Newman
    Abstract:

    month-olds' abilities to use visual/auditory correlations to segment a complex speech stream were studied using the head-turn preference procedure (HPP) following video familiarization. When two blended voices of equal loudness were presented, infants could use visual correspondences to reliably recognize words in the video, even in the case of a moving oscilloscope pattern. In contrast, in cases where a static picture was presented, infants did not show an ability to segment the speech stream at this signal to noise ratio.

M Moody - One of the best experts on this subject based on the ideXlab platform.

  • Speech separation by simulating the Cocktail Party Effect with a neural network controlled Wiener filter
    1997 IEEE International Conference on Acoustics Speech and Signal Processing, 1997
    Co-Authors: S Sridharan, M Moody
    Abstract:

    A novel speech separation structure which simulates the Cocktail Party Effect using a modified iterative Wiener filter and a multi-layer perceptron neural network is presented. The neural network is used as a speaker recognition system to control the iterative Wiener filter. The neural network is a modified perceptron with a hidden layer using feature data extracted from LPC cepstral analysis. The proposed technique has been successfully used for speech separation when the interference is competing speech or broad band noise.

  • ICASSP - Speech separation by simulating the Cocktail Party Effect with a neural network controlled Wiener filter
    1997 IEEE International Conference on Acoustics Speech and Signal Processing, 1997
    Co-Authors: S Sridharan, M Moody
    Abstract:

    A novel speech separation structure which simulates the Cocktail Party Effect using a modified iterative Wiener filter and a multi-layer perceptron neural network is presented. The neural network is used as a speaker recognition system to control the iterative Wiener filter. The neural network is a modified perceptron with a hidden layer using feature data extracted from LPC cepstral analysis. The proposed technique has been successfully used for speech separation when the interference is competing speech or broad band noise.

  • co talker separation using the Cocktail Party Effect
    Journal of The Audio Engineering Society, 1996
    Co-Authors: S Sridharan, M Moody
    Abstract:

    An artificial neural network (ANN) speech-classifier-controlled iterative filtering system is described, which simulates the Cocktail Party Effect for speech separation. The ANN speech classifier controls a modified iterative Wiener filter to cancel the interference by setting the filter's parameters and the convergence criterion for the iteration. The proposed system has been employed successfully with multiple-microphone speech acquisition systems for co-talker speech separation. The simulation results have shown that the iterative processing controlled by the neural network consistently provides speech of good quality and intelligibility.

  • ISSPA - Speech Enhancement Iby Simulation Of Cocktail Party Effect With Neural Network Controlled Iterative Filter
    1996
    Co-Authors: S Sridharan, M Moody
    Abstract:

    This paper describes a novel speech enhancement structure which simulates the Cocktail Party Effect using a modified iterative Wiener filter and a multi-layer perceptron neural network. The key idea is to use the neural network as a speaker recognition system to control the iterative Wiener filter. The neural network is a modified perceptron with a hidden layer using feature data extracted from LPC cepstral analysis. The proposed technique has been successfully used for speech enhancement when the interference is competing speech or broad band noise.

  • speech enhancement iby simulation of Cocktail Party Effect with neural network controlled iterative filter
    Information Sciences Signal Processing and their Applications, 1996
    Co-Authors: S Sridharan, M Moody
    Abstract:

    This paper describes a novel speech enhancement structure which simulates the Cocktail Party Effect using a modified iterative Wiener filter and a multi-layer perceptron neural network. The key idea is to use the neural network as a speaker recognition system to control the iterative Wiener filter. The neural network is a modified perceptron with a hidden layer using feature data extracted from LPC cepstral analysis. The proposed technique has been successfully used for speech enhancement when the interference is competing speech or broad band noise.

S Sridharan - One of the best experts on this subject based on the ideXlab platform.

  • Speech separation by simulating the Cocktail Party Effect with a neural network controlled Wiener filter
    1997 IEEE International Conference on Acoustics Speech and Signal Processing, 1997
    Co-Authors: S Sridharan, M Moody
    Abstract:

    A novel speech separation structure which simulates the Cocktail Party Effect using a modified iterative Wiener filter and a multi-layer perceptron neural network is presented. The neural network is used as a speaker recognition system to control the iterative Wiener filter. The neural network is a modified perceptron with a hidden layer using feature data extracted from LPC cepstral analysis. The proposed technique has been successfully used for speech separation when the interference is competing speech or broad band noise.

  • ICASSP - Speech separation by simulating the Cocktail Party Effect with a neural network controlled Wiener filter
    1997 IEEE International Conference on Acoustics Speech and Signal Processing, 1997
    Co-Authors: S Sridharan, M Moody
    Abstract:

    A novel speech separation structure which simulates the Cocktail Party Effect using a modified iterative Wiener filter and a multi-layer perceptron neural network is presented. The neural network is used as a speaker recognition system to control the iterative Wiener filter. The neural network is a modified perceptron with a hidden layer using feature data extracted from LPC cepstral analysis. The proposed technique has been successfully used for speech separation when the interference is competing speech or broad band noise.

  • co talker separation using the Cocktail Party Effect
    Journal of The Audio Engineering Society, 1996
    Co-Authors: S Sridharan, M Moody
    Abstract:

    An artificial neural network (ANN) speech-classifier-controlled iterative filtering system is described, which simulates the Cocktail Party Effect for speech separation. The ANN speech classifier controls a modified iterative Wiener filter to cancel the interference by setting the filter's parameters and the convergence criterion for the iteration. The proposed system has been employed successfully with multiple-microphone speech acquisition systems for co-talker speech separation. The simulation results have shown that the iterative processing controlled by the neural network consistently provides speech of good quality and intelligibility.

  • ISSPA - Speech Enhancement Iby Simulation Of Cocktail Party Effect With Neural Network Controlled Iterative Filter
    1996
    Co-Authors: S Sridharan, M Moody
    Abstract:

    This paper describes a novel speech enhancement structure which simulates the Cocktail Party Effect using a modified iterative Wiener filter and a multi-layer perceptron neural network. The key idea is to use the neural network as a speaker recognition system to control the iterative Wiener filter. The neural network is a modified perceptron with a hidden layer using feature data extracted from LPC cepstral analysis. The proposed technique has been successfully used for speech enhancement when the interference is competing speech or broad band noise.

  • speech enhancement iby simulation of Cocktail Party Effect with neural network controlled iterative filter
    Information Sciences Signal Processing and their Applications, 1996
    Co-Authors: S Sridharan, M Moody
    Abstract:

    This paper describes a novel speech enhancement structure which simulates the Cocktail Party Effect using a modified iterative Wiener filter and a multi-layer perceptron neural network. The key idea is to use the neural network as a speaker recognition system to control the iterative Wiener filter. The neural network is a modified perceptron with a hidden layer using feature data extracted from LPC cepstral analysis. The proposed technique has been successfully used for speech enhancement when the interference is competing speech or broad band noise.

Rebecca M Morley - One of the best experts on this subject based on the ideXlab platform.

  • asymmetric performance in the Cocktail Party Effect implications for the design of spatial audio displays
    Human Factors, 2001
    Co-Authors: Robert S. Bolia, Todd W Nelson, Rebecca M Morley
    Abstract:

    An experiment was conducted to determine the extent to which hemispheric specialization is manifested in the performance of tasks in which listeners are required to attend to one of several simultaneously spoken speech communications. Speech intelligibility and response time were measured under factorial combinations of the number of simultaneous talkers, the target talker hemifield, and the spatial arrangement of talkers. Intelligibility was found to be mediated by all of the independent variables. Results are discussed in terms of the design of adaptive spatial audio interfaces for speech communications. Actual or potential applications of this research include the design of adaptive spatial audio interfaces for speech communications.

William T Freeman - One of the best experts on this subject based on the ideXlab platform.

  • ICMI - Ausio-visual Segmentation and The Cocktail Party Effect
    Advances in Multimodal Interfaces — ICMI 2000, 2020
    Co-Authors: Trevor Darrell, John W Fisher, Paul A Viola, William T Freeman
    Abstract:

    Audio-based interfaces usually suffer when noise or other acoustic sources are present in the environment. For robust audio recognition, a single source must first be isolated. Existing solutions to this problem generally require special microphone configurations, and often assume prior knowledge of the spurious sources. We have developed new algorithms for segmenting streams of audio-visual information into their constituent sources by exploiting the mutual information present between audio and visual tracks. Automatic face recognition and image motion analysis methods are used to generate visual features for a particular user; empirically these features have high mutual information with audio recorded from that user. We show how audio utterances from several speakers recorded with a single microphone can be separated into constituent streams; we also show how the method can help reduce the Effect of noise in automatic speech recognition.

  • ausio visual segmentation and the Cocktail Party Effect
    International Conference on Multimodal Interfaces, 2000
    Co-Authors: Trevor Darrell, John W Fisher, Paul A Viola, William T Freeman
    Abstract:

    Audio-based interfaces usually suffer when noise or other acoustic sources are present in the environment. For robust audio recognition, a single source must first be isolated. Existing solutions to this problem generally require special microphone configurations, and often assume prior knowledge of the spurious sources. We have developed new algorithms for segmenting streams of audio-visual information into their constituent sources by exploiting the mutual information present between audio and visual tracks. Automatic face recognition and image motion analysis methods are used to generate visual features for a particular user; empirically these features have high mutual information with audio recorded from that user. We show how audio utterances from several speakers recorded with a single microphone can be separated into constituent streams; we also show how the method can help reduce the Effect of noise in automatic speech recognition.