Identification Of Word Boundaries Research Articles

Eye movements of second language learners when reading spaced and unspaced Chinese texts.

Unlike English, Chinese does not have interword spacing in written texts, which poses difficulties for Chinese-as-a-second-language (CSL) learners' identification of word boundaries and affects their reading comprehension and vocabulary acquisition. The eye-movement literature has suggested that interword spacing is important in alphabetic languages; examining languages that lack interword spaces such as Chinese, thus, may help to inform theoretical accounts of eye-movement control and word identification during reading. Research investigating the interword spacing effect in reading Chinese showed that adding spacing facilitated CSL learners' reading comprehension and speed as well as vocabulary learning. However, the bulk of this research mainly looked at the learning outcomes (off-line measures), with few studies focusing on L2 learners' reading processes. Building on this background, this study seeks to provide a descriptive perspective of the eye movements of CSL learners. In this study, 24 CSL learners with intermediate Chinese proficiency were recruited as the experimental group, and 20 Chinese native speakers were recruited as the control group. The EyeLink 1,000 eye tracker was used to record their reading of four segmentation conditions of Chinese texts, namely, no space condition, word-spaced condition, non-word-spaced condition, and pinyin-spaced condition. Results show that: (1) CSL learners with intermediate Chinese proficiency generally spent less time reading Chinese texts with spaces between words, and they showed more gazes and regressions when reading texts without spaces; (2) Non-word-spaced texts and Pinyin-spaced texts interfere with CSL learners' reading process; and (3) Intermediate CSL learners show consistent eye movement patterns in the normal no-space condition and word-spaced condition. I conclude that word boundary information can effectively guide CSL learners' eye movement behaviors and eye saccade planning, thus improving reading efficiency.

Lack of neural evidence for implicit language learning in 9-month-old infants at high risk for autism.

Word segmentation is a fundamental aspect of language learning, since identification of word boundaries in continuous speech must occur before the acquisition of word meanings can take place. We previously used functional magnetic resonance imaging (fMRI) to show that youth with autism spectrum disorder (ASD) are less sensitive to statistical and speech cues that guide implicit word segmentation. However, little is known about the neural mechanisms underlying this process during infancy and how this may be associated with ASD risk. Here, we examined early neural signatures of language-related learning in 9-month-old infants at high (HR) and low familial risk (LR) for ASD. During natural sleep, infants underwent fMRI while passively listening to three speech streams containing strong statistical and prosodic cues, strong statistical cues only, or minimal statistical cues to word boundaries. Compared to HR infants, LR infants showed greater activity in the left amygdala for the speech stream containing statistical and prosodic cues. While listening to this same speech stream, LR infants also showed more learning-related signal increases in left temporal regions as well as increasing functional connectivity between bilateral primary auditory cortex and right anterior insula. Importantly, learning-related signal increases at 9 months positively correlated with expressive language outcome at 36 months in both groups. In the HR group, greater signal increases were additionally associated with less severe ASD symptomatology at 36 months. These findings suggest that early differences in the neural networks underlying language learning may predict subsequent language development and altered trajectories associated with ASD risk.

Vietnamese Short Text Classification via Distributed Computation

Social networking has been growing rapidly in Vietnam. The sharing information is diverse and circulates in many forms. It requires user-friendly solutions such as topic sorting and perspectives analysis in analyzing community trends, advertisements or anticipating and monitoring the spread of bad news. Unfortunately, Vietnamese is highly different from other languages and little research has been conducted in the literature on messages classification. The implementation of machine learning models on Vietnamese has not been thoroughly investigated and these models’ performance is unknown when applying in a different language. Vietnamese text is a serialization of syllables, hence, word boundary identification is not trivial. This research portrays our endeavor to construct an effective distributed framework for addressing the task of classification of short Vietnamese texts on social networks using the idea of probability categorization. The authors argue that addressing the task sharps the successful combination of machine learning, nat-ural language processing, and ambient intelligence. The proposed framework is effective and enables fast calculation, suitable for implementation in Apache Spark, meeting the demand for dealing with large amounts of textual data on the current social networks. Our data has been collected from several online text sources of 12412 short messages classified into 5 different topics. The evaluation shows that our approach has achieved an average of 82.73% classification accuracy. Thoughtfully learning the literature, we could state that this is the first attempt to classify short Vietnamese messages under a distributed computation framework.

The use of lexical and sublexical cues in speech segmentation by second language learners of English

Abstract This study examined the use of lexical and sublexical cues in speech segmentation by Mandarin L2 learners of English, focusing on two types of lexical cue, lexical knowledge and semantic relatedness, and three coda (sublexical) cues, /n, s, ŋ/ due to their varying phonotactic probabilities in Mandarin and English. Thirty-five native English speakers and 30 L2 learners participated in two experiments. Experiment 1 showed that learners were able to use lexicality as a cue to segment L2 speech. The lexicality effect significantly interacted with L2 proficiency. Experiment 2 showed that learners did not use semantic cues to the same extent as native listeners did. All participants experienced more difficulty with word boundary identification preceded by /s/. This difficulty may stem from weak allophonic cues of /s/ in English. L2 learners with better proficiency may be better at recognizing familiar words from continuous speech, thus more efficiently utilizing the lexicality cue.

No Neural Evidence of Statistical Learning During Exposure to Artificial Languages in Children with Autism Spectrum Disorders

Language delay is a hallmark feature of autism spectrum disorders (ASD). The identification of word boundaries in continuous speech is a critical first step in language acquisition that can be accomplished via statistical learning and reliance on speech cues. Importantly, early word segmentation skills have been shown to predict later language development in typically developing (TD) children. Here we investigated the neural correlates of online word segmentation in children with and without ASD with a well-established behavioral paradigm previously validated for functional magnetic resonance imaging. Eighteen high-functioning boys with ASD and 18 age- and IQ-matched TD boys underwent functional magnetic resonance imaging while listening to two artificial languages (containing statistical or statistical + prosodic cues to word boundaries) and a random speech stream. Consistent with prior findings, in TD control subjects, activity in fronto-temporal-parietal networks decreased as the number of cues to word boundaries increased. The ASD children, however, did not show this facilitatory effect. Furthermore, statistical contrasts modeling changes in activity over time identified significant learning-related signal increases for both artificial languages in basal ganglia and left temporo-parietal cortex only in TD children. Finally, the level of communicative impairment in ASD children was inversely correlated with signal increases in these same regions during exposure to the artificial languages. This is the first study to demonstrate significant abnormalities in the neural architecture subserving language-related learning in ASD children and to link the communicative impairments observed in this population to decreased sensitivity to the statistical and speech cues available in the language input.

Effect of Speaking Rate on the Identification of Word Boundaries

Two experiments were conducted to determine whether listeners’ ability to use allophonic variation to identify word boundaries is influenced by speaking rate. Listeners in both experiments were presented two-word sequences (such as great eyes) spoken by naturally fast and naturally slow talkers; in one experiment the sequences were presented in quiet and in the other they were presented in noise. The listeners’ task was to identify the intended sequence from among four choices with alternative segmentations (e.g. great eyes, gray ties, great ties, gray eyes). In both experiments performance was worse for the sequences produced by the naturally fast talkers than for those produced by the naturally slow talkers. This finding suggests that the extent to which allophonic variation contributes to the identification of word boundaries may depend on the rate at which the speech was produced.

Statistical-Based Approach to Non-segmented Language Processing

Several approaches have been studied to cope with the exceptional features of non-segmented languages. When there is no explicit information about the boundary of a word, segmenting an input text is a formidable task in language processing. Not only the contemporary word list, but also usages of the words have to be maintained to cover the use in the current texts. The accuracy and efficiency in higher processing do heavily rely on this word boundary identification task. In this paper, we introduce some statistical based approaches to tackle the problem due to the ambiguity in word segmentation. The word boundary identification problem is then defined as a part of others for performing the unified language processing in total. To exhibit the ability in conducting the unified language processing, we selectively study the tasks of language identification, word extraction, and dictionary-less search engine.

An exploration of listener strategies in the lexical segmentation of hypokinetic dysarthric speech

This investigation examined listener transcriptions of phrases produced by speakers with mild to severe hypokinetic dysarthria to examine individual strategies for the identification of word boundaries in connected speech. It was hypothesized that the most efficient listeners would use syllabic strength information to guide their parsing of the continuous acoustic stream. Six-hundred transcribed phrases (10 listeners ×60 phrases) were coded independently by two judges to identify (1) correct word transcriptions, (2) evidence of accurate lexical parsing, regardless of exact word identification, and (3) the proportions of accurate parsing of strong and weak syllable word onsets. Linear regression analysis of the group data revealed that correct segmentation of strong syllable word onsets predicted listener performance on segmenting weak syllable word onsets [R=0.805, F(1,29)=51.440, p<0.001]. Despite a wide range of listener performance on ``words correct,'' individual strategies for perceptual segmentation were evident only in the transcriptions for the most severe speaker. In this case, the poorest listeners exhibited disproportionate difficulty with the segmentation weak syllable word onsets. Results suggest that a listener's ability to use syllabic strength information in lexical parsing determines, in part, their ability to recognize word onsets. [Work supported by NIDCD, NIH.]

Syllable duration and word boundaries in French: A preliminary report

Requisite to understanding a spoken message is the ability to recognize the grouping of segments into syllables and syllables into words. The durations of vowels and consonants are known to vary with their positions in words and phrases, with segments in final syllables being longer than those in nonfinal syllables. Furthermore, relative duration, or rhythm, has been shown to be important in identifying word-boundary location in English noun phrases [L. H. Nakatani and K. Shaffer, J. Acoust. Soc. Am. 63, 234–245 (1978)]. However, the situation in French, a ‘‘syllable-timed’’ language, is less clear. Durational data will be reported for sets of three-syllable utterances of one, two, and three words, produced in isolation and within sentences by three native speakers of French, as part of this study to identify the cues to word-boundary identification in a nonstress-timed language. [Work supported by St. John’s Univsersity and by NIDCD Grant No. DC-00121 to the Haskins Labs.]