Variability in bimodal spoken language processing by native and nonnative speakers of English: A closer look at effects of speech style

2005 ◽  
Vol 46 (1) ◽  
pp. 73-93 ◽  
Author(s):  
Debra M. Hardison
Keyword(s):  
Language Processing ◽  
Nonnative Speakers ◽  
Spoken Language ◽  
Spoken Language Processing ◽  
Nonnative Speakers Of English

Children With Cochlear Implants Use Semantic Prediction to Facilitate Spoken Word Recognition

2021 ◽  
pp. 1-14
Author(s):  
Christina Blomquist ◽  
Rochelle S. Newman ◽  
Yi Ting Huang ◽  
Jan Edwards
Keyword(s):  
Word Recognition ◽  
Target Word ◽  
Cochlear Implants ◽  
Language Processing ◽  
Spoken Word Recognition ◽  
Spoken Language ◽  
Spoken Word ◽  
Semantic Cues ◽  
Matched Group ◽  
Spoken Language Processing

Purpose Children with cochlear implants (CIs) are more likely to struggle with spoken language than their age-matched peers with normal hearing (NH), and new language processing literature suggests that these challenges may be linked to delays in spoken word recognition. The purpose of this study was to investigate whether children with CIs use language knowledge via semantic prediction to facilitate recognition of upcoming words and help compensate for uncertainties in the acoustic signal. Method Five- to 10-year-old children with CIs heard sentences with an informative verb ( draws ) or a neutral verb ( gets ) preceding a target word ( picture ). The target referent was presented on a screen, along with a phonologically similar competitor ( pickle ). Children's eye gaze was recorded to quantify efficiency of access of the target word and suppression of phonological competition. Performance was compared to both an age-matched group and vocabulary-matched group of children with NH. Results Children with CIs, like their peers with NH, demonstrated use of informative verbs to look more quickly to the target word and look less to the phonological competitor. However, children with CIs demonstrated less efficient use of semantic cues relative to their peers with NH, even when matched for vocabulary ability. Conclusions Children with CIs use semantic prediction to facilitate spoken word recognition but do so to a lesser extent than children with NH. Children with CIs experience challenges in predictive spoken language processing above and beyond limitations from delayed vocabulary development. Children with CIs with better vocabulary ability demonstrate more efficient use of lexical-semantic cues. Clinical interventions focusing on building knowledge of words and their associations may support efficiency of spoken language processing for children with CIs. Supplemental Material https://doi.org/10.23641/asha.14417627

Spoken language comprehension: insights from eye movements

2007 ◽  
pp. 308-326 ◽  
Author(s):  
Michael K. Tanenhaus
Keyword(s):  
Eye Movements ◽  
Language Processing ◽  
Language Comprehension ◽  
Language Production ◽  
World Literature ◽  
Spoken Language ◽  
Response Measure ◽  
Visual World ◽  
Spoken Language Processing ◽  
Spoken Language Comprehension

Recently, eye movements have become a widely used response measure for studying spoken language processing in both adults and children, in situations where participants comprehend and generate utterances about a circumscribed “Visual World” while fixation is monitored, typically using a free-view eye-tracker. Psycholinguists now use the Visual World eye-movement method to study both language production and language comprehension, in studies that run the gamut of current topics in language processing. Eye movements are a response measure of choice for addressing many classic questions about spoken language processing in psycholinguistics. This article reviews the burgeoning Visual World literature on language comprehension, highlighting some of the seminal studies and examining how the Visual World approach has contributed new insights to our understanding of spoken word recognition, parsing, reference resolution, and interactive conversation. It considers some of the methodological issues that come to the fore when psycholinguists use eye movements to examine spoken language comprehension.

Audio-visual spoken language processing

2004 ◽  
Author(s):  
Jinyoung Kim ◽  
Jeesun Kim ◽  
Chris Davis
Keyword(s):  
Language Processing ◽  
Spoken Language ◽  
Spoken Language Processing

scholarly journals A Hybrid Spoken Language Processing System for Smart Device Troubleshooting

Electronics ◽  
2019 ◽  
Vol 8 (6) ◽  
pp. 681 ◽  
Author(s):  
Praveen Edward James ◽  
Hou Kit Mun ◽  
Chockalingam Aravind Vaithilingam
Keyword(s):  
Language Processing ◽  
Short Term Memory ◽  
Processing System ◽  
Memory Cell ◽  
Spoken Language ◽  
Smart Device ◽  
Short Term ◽  
Language Processor ◽  
Spoken Language Processing ◽  
Long Short Term Memory

The purpose of this work is to develop a spoken language processing system for smart device troubleshooting using human-machine interaction. This system combines a software Bidirectional Long Short Term Memory Cell (BLSTM)-based speech recognizer and a hardware LSTM-based language processor for Natural Language Processing (NLP) using the serial RS232 interface. Mel Frequency Cepstral Coefficient (MFCC)-based feature vectors from the speech signal are directly input into a BLSTM network. A dropout layer is added to the BLSTM layer to reduce over-fitting and improve robustness. The speech recognition component is a combination of an acoustic modeler, pronunciation dictionary, and a BLSTM network for generating query text, and executes in real time with an 81.5% Word Error Rate (WER) and average training time of 45 s. The language processor comprises a vectorizer, lookup dictionary, key encoder, Long Short Term Memory Cell (LSTM)-based training and prediction network, and dialogue manager, and transforms query intent to generate response text with a processing time of 0.59 s, 5% hardware utilization, and an F1 score of 95.2%. The proposed system has a 4.17% decrease in accuracy compared with existing systems. The existing systems use parallel processing and high-speed cache memories to perform additional training, which improves the accuracy. However, the performance of the language processor has a 36.7% decrease in processing time and 50% decrease in hardware utilization, making it suitable for troubleshooting smart devices.

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