Corpus callosum size and very preterm birth: relationship to neuropsychological outcome

Thinning of the corpus callosum (CC) is often observed in individuals who were born very preterm. Damage to the CC during neurodevelopment may be associated with poor neuropsychological performance. This study aimed to explore any evidence of CC pathology in adolescents aged 14-15 years who were born very preterm, and to investigate the relationship between CC areas and verbal skills. Seventy-two individuals born before 33 weeks of gestation and 51 age- and sex-matched full-term controls received structural MRI and neuropsychological assessment. Total CC area in very preterm adolescents was 7.5% smaller than in controls, after adjusting for total white matter volume (P=0.015). The absolute size of callosal subregions differed between preterm and fullterm adolescents: preterm individuals had a 14.7% decrease in posterior (P<0.0001) and an 11.6% decrease in mid-posterior CC quarters (P=0.029). Preterm individuals who had experienced periventricular haemorrhage and ventricular dilatation in the neonatal period showed the greatest decrease in CC area. In very preterm boys only, verbal IQ and verbal fluency scores were positively associated with total mid-sagittal CC size and midposterior surface area. These results suggest that very preterm birth adversely affects the development of the CC, particularly its posterior quarter, and this impairs verbal skills in boys.

A Corpus-Based Approach to Speech Enhancement from Nonstationary Noise

Temporal dynamics and speaker characteristics are two important features of speech that distinguish speech from noise. In this paper, we propose a method to maximally extract these two features of speech for speech enhancement. We demonstrate that this can reduce the requirement for prior information about the noise, which can be difficult to estimate for fast-varying noise. Given noisy speech, the new approach estimates clean speech by recognizing long segments of the clean speech as whole units. In the recognition, clean speech sentences, taken from a speech corpus, are used as examples. Matching segments are identified between the noisy sentence and the corpus sentences. The estimate is formed by using the longest matching segments found in the corpus sentences. Longer speech segments as whole units contain more distinct dynamics and richer speaker characteristics, and can be identified more accurately from noise than shorter speech segments. Therefore, estimation based on the longest recognized segments increases the noise immunity and hence the estimation accuracy. The new approach consists of a statistical model to represent up to sentence-long temporal dynamics in the corpus speech, and an algorithm to identify the longest matching segments between the noisy sentence and the corpus sentences. The algorithm is made more robust to noise uncertainty by introducing missing-feature based noise compensation into the corpus sentences. Experiments have been conducted on the TIMIT database for speech enhancement from various types of nonstationary noise including song, music, and crosstalk speech. The new approach has shown improved performance over conventional enhancement algorithms in both objective and subjective evaluations.

Detection of avian influenza virus by fluorescent DNA barcode-based immunoassay with sensitivity comparable to PCR

In this paper, a coupling of fluorophore-DNA barcode and bead-based immunoassay for detecting avian influenza virus (AIV) with PCR-like sensitivity is reported. The assay is based on the use of sandwich immunoassay and fluorophore-tagged oligonucleotides as representative barcodes. The detection involves the sandwiching of the target AIV between magnetic immunoprobes and barcode-carrying immunoprobes. Because each barcode-carrying immunoprobe is functionalized with a multitude of fluorophore-DNA barcode strands, many DNA barcodes are released for each positive binding event resulting in amplification of the signal. Using an inactivated H16N3 AIV as a model, a linear response over five orders of magnitude was obtained, and the sensitivity of the detection was comparable to conventional RT-PCR. Moreover, the entire detection required less than 2 hr. The results indicate that the method has great potential as an alternative for surveillance of epidemic outbreaks caused by AIV, other viruses and microorganisms.

UNITALK: a corpus of university classroom talk

This is a dataset of recordings and transcriptions of spoken English collected from a range of university classrooms. UNITALK is a modest-sized untagged synchronic specialized full-text corpus of spoken academic discourse collected from fifteen university classrooms. UNITALK was designed to study the genre of small group teaching contexts across academic divisions and subject disciplines and specifically designed to study those teaching events whose goal is to work on collaborative ideas or tasks. The corpus is over 100,000 words and can be used to investigate academic language use and small group university teaching and learning contexts.

The SEMAINE corpus of emotionally coloured character interactions

We have recorded a new corpus of emotionally coloured conversations. Users were recorded while holding conversations with an operator who adopts in sequence four roles designed to evoke emotional reactions. The operator and the user are seated in separate rooms; they see each other through teleprompter screens, and hear each other through speakers. To allow high quality recording, they are recorded by five high-resolution, high framerate cameras, and by four microphones. All sensor information is recorded synchronously, with an accuracy of 25 μs. In total, we have recorded 20 participants, for a total of 100 character conversational and 50 non-conversational recordings of approximately 5 minutes each. All recorded conversations have been fully transcribed and annotated for five affective dimensions and partially annotated for 27 other dimensions. The corpus has been made available to the scientific community through a web-accessible database.

Speech Enhancement from Additive Noise and Channel Distortion - a Corpus-Based Approach

This paper presents a new approach to single-channel speech enhancement involving both noise and channel distortion (i.e., convolutional noise). The approach is based on finding longest matching segments (LMS) from a corpus of clean, wideband speech. The approach adds three novel developments to our previous LMS research. First, we address the problem of channel distortion as well as additive noise. Second, we present an improved method for modeling noise. Third, we present an iterative algorithm for improved speech estimates. In experiments using speech recognition as a test with the Aurora 4 database, the use of our enhancement approach as a preprocessor for feature extraction significantly improved the performance of a baseline recognition system. In another comparison against conventional enhancement algorithms, both the PESQ and the segmental SNR ratings of the LMS algorithm were superior to the other methods for noisy speech enhancement. Index Terms: corpus-based speech model, longest matching segment, speech enhancement, speech recognition