Disfluency detection using a noisy channel model and deep neural language model Article Swipe
Paria Jamshid Lou
,
Mark Johnson
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.25949/19435736
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.25949/19435736
Although speech recognition technology has improved considerably in recent years, current systems still output simply a sequence of words without any useful information about the location of disfluencies. On the other hand, such information is necessary for improving the readability of speech transcripts. In fact, speech transcripts containing a lot of disfluencies are difficult to understand, so removing disfluent words can make speech transcripts more readable. Moreover, many tasks including dialogue systems input spontaneous speech. Such systems are usually trained on fluent, clean corpora, so inputting disfluent data would decrease their performance. This thesis aims at introducing a model for automatic disfluency detection in spontaneous speech transcripts called LSTM Noisy Channel Model. The model uses a Noisy Channel Model (NCM) to find "rough copies" that are likely to indicate disfluencies and generate n-best candidate disfluency analyses. Then, the underlying fluent sentences of each candidate analysis are scored using a Long Short-Term Memory (LSTM) language model. The LSTM language model scores, along with other features, are used in a reranker to identify the most plausible analysis. We show that using LSTM language model scores as features to rerank the analyses generated by an NCM improves the state of-the-art in disfluency detection.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/1808.09091
- https://arxiv.org/pdf/1808.09091
- OA Status
- green
- References
- 10
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W2952294853
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W2952294853Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.25949/19435736Digital Object Identifier
- Title
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Disfluency detection using a noisy channel model and deep neural language modelWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
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2022Year of publication
- Publication date
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2022-01-01Full publication date if available
- Authors
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Paria Jamshid Lou, Mark JohnsonList of authors in order
- Landing page
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https://arxiv.org/abs/1808.09091Publisher landing page
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https://arxiv.org/pdf/1808.09091Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/1808.09091Direct OA link when available
- Concepts
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Computer science, Language model, Artificial intelligence, Channel (broadcasting), Speech recognition, Natural language processing, Computer networkTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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| abstract_inverted_index.analyses | 188 |
| abstract_inverted_index.analysis | 144 |
| abstract_inverted_index.corpora, | 83 |
| abstract_inverted_index.decrease | 89 |
| abstract_inverted_index.dialogue | 70 |
| abstract_inverted_index.features | 184 |
| abstract_inverted_index.generate | 131 |
| abstract_inverted_index.identify | 170 |
| abstract_inverted_index.improved | 5 |
| abstract_inverted_index.improves | 193 |
| abstract_inverted_index.indicate | 128 |
| abstract_inverted_index.language | 153, 157, 180 |
| abstract_inverted_index.location | 25 |
| abstract_inverted_index.removing | 57 |
| abstract_inverted_index.reranker | 168 |
| abstract_inverted_index.sequence | 16 |
| abstract_inverted_index.Moreover, | 66 |
| abstract_inverted_index.analyses. | 135 |
| abstract_inverted_index.analysis. | 174 |
| abstract_inverted_index.automatic | 100 |
| abstract_inverted_index.candidate | 133, 143 |
| abstract_inverted_index.detection | 102 |
| abstract_inverted_index.difficult | 53 |
| abstract_inverted_index.disfluent | 58, 86 |
| abstract_inverted_index.features, | 163 |
| abstract_inverted_index.generated | 189 |
| abstract_inverted_index.improving | 37 |
| abstract_inverted_index.including | 69 |
| abstract_inverted_index.inputting | 85 |
| abstract_inverted_index.necessary | 35 |
| abstract_inverted_index.plausible | 173 |
| abstract_inverted_index.readable. | 65 |
| abstract_inverted_index.sentences | 140 |
| abstract_inverted_index.Short-Term | 150 |
| abstract_inverted_index.containing | 47 |
| abstract_inverted_index.detection. | 199 |
| abstract_inverted_index.disfluency | 101, 134, 198 |
| abstract_inverted_index.of-the-art | 196 |
| abstract_inverted_index.technology | 3 |
| abstract_inverted_index.underlying | 138 |
| abstract_inverted_index.information | 22, 33 |
| abstract_inverted_index.introducing | 96 |
| abstract_inverted_index.readability | 39 |
| abstract_inverted_index.recognition | 2 |
| abstract_inverted_index.spontaneous | 73, 104 |
| abstract_inverted_index.transcripts | 46, 63, 106 |
| abstract_inverted_index.understand, | 55 |
| abstract_inverted_index.considerably | 6 |
| abstract_inverted_index.disfluencies | 51, 129 |
| abstract_inverted_index.performance. | 91 |
| abstract_inverted_index.transcripts. | 42 |
| abstract_inverted_index.disfluencies. | 27 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 2 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.8500000238418579 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.00193293 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |