Word segmentation from noise-band vocoded speech

Grieco-Calub, Tina M.; Simeon, Katherine M.; Snyder, Hillary E.; Lew‐Williams, Casey

doi:10.1080/23273798.2017.1354129

Cited by 6 publications

(3 citation statements)

References 77 publications

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“…For example, Barakat, Seitz, and Shams () reported that the presence of statistical regularities enhances the detection of individual visual elements even when they appear outside the context of the learned regularities. In a similar vein, Grieco‐Calub, Simeon, Snyder, and Lew‐Williams () showed that auditory SL performance is impaired when the familiarization stream comprises spectrally degraded speech sounds. From a broader perspective, the interaction between encoding and learning aligns with the well‐documented benefits of context on stimulus encoding, such as the classic effect of schema congruency on subsequent memory of new events (Hintzman, ).…”

Section: Introductionmentioning

confidence: 89%

“…This interaction suggested that the ability to encode the individual shapes and the sensitivity to their co-occurrences are not independent processes, but rather that the statistical properties of the stream may facilitate encoding, and conversely, that optimal conditions of encoding can serve to enhance sensitivity to the statistical structure of the input. In a similar vein, Grieco-Calub, Simeon, Snyder, and Lew-Williams (2017) showed that auditory SL performance is impaired when the familiarization stream comprises spectrally degraded speech sounds. Participants who showed greater sensitivity to changes in ED tended to show greater sensitivity to changes in TPs, and vice versa.…”

Section: Introductionmentioning

confidence: 90%

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What Determines Visual Statistical Learning Performance? Insights From Information Theory

2019

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In order to extract the regularities underlying a continuous sensory input, the individual elements constituting the stream have to be encoded and their transitional probabilities (TPs) should be learned. This suggests that variance in statistical learning (SL) performance reflects efficiency in encoding representations as well as efficiency in detecting their statistical properties. These processes have been taken to be independent and temporally modular, where first, elements in the stream are encoded into internal representations, and then the co-occurrences between them are computed and registered. Here, we entertain a novel hypothesis that one unifying construct-the rate of information in the sensory input-explains learning performance. This theoretical approach merges processes related to encoding of events and those related to learning their regularities into a single computational principle. We present data from two large-scale experiments with over 800 participants tested in support for this hypothesis, showing that rate of information in a visual stream clearly predicts SL performance, and that similar rate of information values leads to similar SL performance. We discuss the implications for SL theory and its relation to regularity learning.

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Section: Introductionmentioning

confidence: 89%

Section: Introductionmentioning

confidence: 90%

What Determines Visual Statistical Learning Performance? Insights From Information Theory

2019

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“…They have also been used to outline how interactions between channels in the CI signal impact speech recognition (Grange et al, 2017;Oxenham and Kreft, 2014), and how degraded speech signals may impact language development (e.g. Newman et al 2020) and listening effort (Milvae et al, 2021;Winn et al, 2015), as well as the auditory pattern recognition skills that support word learning (Grieco-Calub et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

How to vocode: Using channel vocoders for cochlear-implant research

Cychosz¹,

Winn²,

Goupell³

2023

Preprint

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The channel vocoder has become a useful tool to understand the impact of specific forms of auditory degradation---particularly the spectral and temporal degradation that reflect cochlear implant processing. Vocoders have many parameters that allow researchers to answer questions about cochlear implant processing in ways that overcome some logistical complications of controlling for factors in individual cochlear implant users. However, there is such a large variety in the implementation of vocoders that the term "vocoder" is not specific enough to describe the signal processing used in these experiments. Misunderstanding vocoder parameters can result in experimental confounds or unexpected stimulus distortions. This paper will highlight the signal processing parameters that should be specified when describing vocoder construction. The paper also provides guidance on how to determine vocoder parameters within perception experiments given the experimenter's goals and research questions to avoid common signal processing mistakes. Throughout, we will take the assumption that experimenters are interested in vocoders with the specific goal of better understanding cochlear implants.

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Prosody-driven syntax learning is robust to impoverished pitch and spectral cues

Hawthorne

2018

The Journal of the Acoustical Society of America

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Across languages, prosodic boundaries tend to align with syntactic boundaries, and both infant and adult language learners capitalize on these correlations to jump-start syntax acquisition. However, it is unclear which prosodic cues-pauses, final-syllable lengthening, and/or pitch resets across boundaries-are necessary for prosodic bootstrapping to occur. It is also unknown how syntax acquisition is impacted when listeners do not have access to the full range of prosodic or spectral information. These questions were addressed using 14-channel noise-vocoded (spectrally degraded) speech. While pre-boundary lengthening and pauses are well-transmitted through noise-vocoded speech, pitch is not; overall intelligibility is also decreased. In two artificial grammar experiments, adult native English speakers showed a similar ability to use English-like prosody to bootstrap unfamiliar syntactic structures from degraded speech and natural, unmanipulated speech. Contrary to previous findings that listeners may require pitch resets and final lengthening to co-occur if no pause cue is present, participants in the degraded speech conditions were able to detect prosodic boundaries from lengthening alone. Results suggest that pitch is not necessary for adult English speakers to perceive prosodic boundaries associated with syntactic structures, and that prosodic bootstrapping is robust to degraded spectral information.

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Word segmentation from noise-band vocoded speech

Cited by 6 publications

References 77 publications

What Determines Visual Statistical Learning Performance? Insights From Information Theory

What Determines Visual Statistical Learning Performance? Insights From Information Theory

How to vocode: Using channel vocoders for cochlear-implant research

Prosody-driven syntax learning is robust to impoverished pitch and spectral cues

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