A multi-pass linear fold algorithm for sentence boundary detection using prosodic cues

Wang, Dagen; Narayanan, Shrikanth

doi:10.1109/icassp.2004.1326038

Cited by 12 publications

(2 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, they have not typically focused on identifying IU boundaries in everyday conversations. Many works (e.g., Stolcke and Shriberg, 1996, Wang and Narayanan, 2004, and Liu et al, 2006 use the Switchboard corpus to identify syntactically-based prosodic boundaries in telephone conversations between strangers, using orthographic inputs and/or manually crafted acoustic features. Xu et al (2014) applies pause, pitch, energy, and duration information to a similar task in spoken Mandarin.…”

Section: Introductionmentioning

confidence: 99%

PSST! Prosodic Speech Segmentation with Transformers

Roll,

Graham,

Todd

2023

Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL)

View full text Add to dashboard Cite

We develop and probe a model for detecting the boundaries of prosodic chunks in untranscribed conversational English speech. The model is obtained by fine-tuning a Transformer-based speech-to-text (STT) model to integrate the identification of Intonation Unit (IU) boundaries with the STT task. The model shows robust performance, both on held-out data and on out-of-distribution data representing different dialects and transcription protocols. By evaluating the model on degraded speech data, and comparing it with alternatives, we establish that it relies heavily on lexico-syntactic information inferred from audio, and not solely on acoustic information typically understood to cue prosodic structure. We release our model 1 as both a transcription tool and a baseline for further improvements in prosodic segmentation.

show abstract

Section: Introductionmentioning

confidence: 99%

PSST! Prosodic Speech Segmentation with Transformers

Roll,

Graham,

Todd

2023

Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL)

View full text Add to dashboard Cite

show abstract

mentioning

confidence: 99%

Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL)

2023

View full text Add to dashboard Cite

Language models (LMs) have been argued to overlap substantially with human beings in grammaticality judgment tasks. But when humans systematically make errors in language processing, should we expect LMs to behave like cognitive models of language and mimic human behavior? We answer this question by investigating LMs' more subtle judgments associated with "language illusions" -sentences that are vague in meaning, implausible, or ungrammatical but receive unexpectedly high acceptability judgments by humans. We looked at three illusions: the comparative illusion (e.g. "More people have been to Russia than I have"), the depth-charge illusion (e.g. "No head injury is too trivial to be ignored"), and the negative polarity item (NPI) illusion (e.g. "The hunter who no villager believed to be trustworthy will ever shoot a bear"). We found that probabilities represented by LMs were more likely to align with human judgments of being "tricked" by the NPI illusion which examines a structural dependency, compared to the comparative and the depth-charge illusions which require sophisticated semantic understanding. No single LM or metric yielded results that are entirely consistent with human behavior. Ultimately, we show that LMs are limited both in their construal as cognitive models of human language processing and in their capacity to recognize nuanced but critical information in complicated language materials.

show abstract

Comparing automatic rich transcription for Portuguese, Spanish and English Broadcast News

Batista

Trancoso

Mamede

2009

2009 IEEE Workshop on Automatic Speech Recognition &Amp; Understanding

View full text Add to dashboard Cite

This paper describes and evaluates a language independent approach for automatically enriching the speech recognition output with punctuation marks and capitalization information. The two tasks are treated as two classification problems, using a maximum entropy modeling approach, which achieves results within state-of-the-art. The language independence of the approach is attested with experiments conducted on Portuguese, Spanish and English Broadcast News corpora. This paper provides the first comparative study between the three languages, concerning these tasks.

show abstract

A multi-pass linear fold algorithm for sentence boundary detection using prosodic cues

Cited by 12 publications

References 5 publications

PSST! Prosodic Speech Segmentation with Transformers

PSST! Prosodic Speech Segmentation with Transformers

Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL)

Comparing automatic rich transcription for Portuguese, Spanish and English Broadcast News

Contact Info

Product

Resources

About