England, Neo-Latin, and the Continental Journey: Linguistic and Textual Itineraries

This chapter examines the ramifications of continental travel and associated epistolary communication for English poets of the period. It argues that recourse to neo-Latin, the universal language of diplomacy, served not only to establish a sense of shared space—linguistic, cultural, generic—between England and the continent, but also to signal self-conscious differences (climatic, geographical, historical, political) between England and her continental peers. Through an investigation of a range of ‘performances’ on stages that were ‘academic’, poetic, autobiographical, and epistolographic, it assesses the central role of neo-Latin as a language that underwent a series of textual itineraries. These ‘itineraries’ manifest themselves in a number of ways. Neo-Latin as a shared linguistic medium can facilitate, and quite uniquely so, intertextual engagement with the classics, but now ancient Rome, its language, its mythology, its hierarchy of genres, are viewed through a seventeenth-century lens and appropriated by poets in both England and Italy to describe contemporary events, whether personal, or political. Close examination of the neo-Latin poetry of Milton and Marvell reveals, it is argued, a self-fashioning coloured by such textual itineraries and interchanges. The absorption and replication of continental literary and linguistic methodologies (the academic debate; the etymological play of Marinism; the hybridity of neo-Latin and Italian voices) reveal in short a linguistic and textual reciprocity that gave birth to something very new.

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

Evidence of syntactic working memory usage in MEG data

While reading times are often used to measure working memory load, frequency effects (such as surprisal or n-gram frequencies) also have strong confounding effects on reading times. This work uses a naturalistic audio corpus with magnetoencephalographic (MEG) annotations to measure working memory load during sentence processing. Alpha oscillations in posterior regions of the brain have been found to correlate with working memory load in non-linguistic tasks (Jensen et al., 2002), and the present study extends these findings to working memory load caused by syntactic center embeddings. Moreover, this work finds that frequency effects in naturally-occurring stimuli do not significantly contribute to neural oscillations in any frequency band, which suggests that many modeling claims could be tested on this sort of data even without controlling for frequency effects.