On the syllabification of phonemes

Bartlett, Susan; Kondrak, Grzegorz; Cherry, Colin

doi:10.3115/1620754.1620799

Cited by 74 publications

(41 citation statements)

References 18 publications

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“…This project therefore errs on the side of undercorrection. Note, however, that one should not compare results to Bartlett et al (2009) 22 We take all lyric poetry from the Mittelhochdeutsche Begriffsdatenbank (MHDBDB) (1992-2017) corpus of medieval German texts. 23 Relevant summary statistics for this subset of the corpus are reproduced in Table 1.…”

Section: Methodsmentioning

confidence: 99%

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Phonological Soundscapes in Medieval Poetry

Hench

2017

Proceedings of the Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanit

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The oral component of medieval poetry was integral to its performance and reception. Yet many believe that the medieval voice has been forever lost, and any attempts at rediscovering it are doomed to failure due to scribal practices, manuscript mouvance, and linguistic normalization in editing practices. This paper offers a method to abstract from this noise and better understand relative differences in phonological soundscapes by considering syllable qualities. The presented syllabification method and soundscape analysis offer themselves as cross-disciplinary tools for low-resource languages. As a case study, we examine medieval German lyric and argue that the heavily debated lyrical 'I' follows a unique trajectory through soundscapes, shedding light on the performance and practice of these poets.

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Section: Methodsmentioning

confidence: 99%

“…Bartlett et al (2009) produced gold standard results of the SSP, LP, and OM, also creating an SVM-HMM model. Adsett and Marchand (2009) test several algorithms across multiple languages concluding that Syllabification by Analogy is most accurate.…”

Section: Syllabificationmentioning

confidence: 99%

Phonological Soundscapes in Medieval Poetry

Hench

2017

Proceedings of the Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanit

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“…Conventionally, syllabification is performed on the sequence of phonemes [25], or directly from the speech signal [26]. However, most of the published techniques do not satisfy causality.…”

Section: A Syllabic Speech Segmentationmentioning

confidence: 99%

“…The incremental phoneme ASR was done by parsing the best partial hypothesis on regular time intervals τ . Asynchronous syllabification (A-SOD system) is then done incrementally with each ASR label, based on the sonority sequencing principle and the syllable onset maximisation [25]. Smaller τ impacts intelligibility of the encoded speech, while larger τ increases the encoding delay.…”

Section: Integrated Encoder and Decodermentioning

confidence: 99%

“…Recent neuroimaging studies of Giraud et al [7] have shown that, during speech processing, the brain generates cortical oscillation in the θ-range (4-7 Hz) that may correspond to the syllable rate, and faster γ-range oscillations (25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40) that correspond to the phonetic scale. This cortical temporal sampling, i.e., packaging information into units of different temporal granularity such as the phonetic level structural units (phonemes) and the syllabic level structural units (syllables), is thought to play a key role in human speech processing.…”

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confidence: 99%

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Incremental Syllable-Context Phonetic Vocoding

Cerňak

Garner

Lazaridis

et al. 2015

IEEE/ACM Trans. Audio Speech Lang. Process.

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Abstract-Current very low bit rate speech coders are, due to complexity limitations, designed to work off-line. This paper investigates incremental speech coding that operates real-time and incrementally (i.e., encoded speech depends only on alreadyuttered speech without the need of future speech information). Since human speech communication is asynchronous (i.e., different information flows being simultaneously processed), we hypothesised that such an incremental speech coder should also operate asynchronously. To accomplish this task, we describe speech coding that reflects the human cortical temporal sampling that packages information into units of different temporal granularity, such as phonemes and syllables, in parallel. More specifically, a phonetic vocoder -cascaded speech recognition and synthesis systems -extended with syllable-based information transmission mechanisms is investigated. There are two main aspects evaluated in this work, the synchronous and asynchronous coding. Synchronous coding refers to the case when the phonetic vocoder and speech generation process depend on the syllable boundaries during encoding and decoding respectively. On the other hand, asynchronous coding refers to the case when the phonetic encoding and speech generation processes are done independently of the syllable boundaries. Our experiments confirmed that the asynchronous incremental speech coding performs better, in terms of intelligibility and overall speech quality, mainly due to better alignment of the segmental and prosodic information. The proposed vocoding operates at an uncompressed bit rate of 213 bits/sec and achieves an average communication delay of 243 ms.

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How are visual words represented? Insights from EEG‐based visual word decoding, feature derivation and image reconstruction

Ling

Lee

Armstrong

et al. 2019

Human Brain Mapping

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Investigations into the neural basis of reading have shed light on the cortical locus and the functional role of visual‐orthographic processing. Yet, the fine‐grained structure of neural representations subserving reading remains to be clarified. Here, we capitalize on the spatiotemporal structure of electroencephalography (EEG) data to examine if and how EEG patterns can serve to decode and reconstruct the internal representation of visually presented words in healthy adults. Our results show that word classification and image reconstruction were accurate well above chance, that their temporal profile exhibited an early onset, soon after 100 ms, and peaked around 170 ms. Further, reconstruction results were well explained by a combination of visual‐orthographic word properties. Last, systematic individual differences were detected in orthographic representations across participants. Collectively, our results establish the feasibility of EEG‐based word decoding and image reconstruction. More generally, they help to elucidate the specific features, dynamics, and neurocomputational principles underlying word recognition.

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On the syllabification of phonemes

Cited by 74 publications

References 18 publications

Phonological Soundscapes in Medieval Poetry

Phonological Soundscapes in Medieval Poetry

Incremental Syllable-Context Phonetic Vocoding

How are visual words represented? Insights from EEG‐based visual word decoding, feature derivation and image reconstruction

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