A hybrid approach to compounds in LVCSR

Laureys, Tom; Vandeghinste, Vincent; Duchateau, Jacques

doi:10.21437/icslp.2002-231

Cited by 4 publications

(1 citation statement)

References 4 publications

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“…They observed that the decompounding method improves precision, recall, and accuracy in morphologically complex European languages. Laureys et al (2002) evaluated the impact of the decompounding module in large vocabulary continuous speech recognition (LVCSR) in Dutch. The decompound module combines a rule-based approach with statistical pruning.…”

Section: Corpus-based Decompounding Methodsmentioning

confidence: 99%

A case study on decompounding in Indian language IR

Sahu,

Pal

2024

Nat. lang. processing

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Decompounding is an essential preprocessing step in text-processing tasks such as machine translation, speech recognition, and information retrieval (IR). Here, the IR issues are explored from five viewpoints. (A) Does word decompounding impact the Indian language IR? If yes, to what extent? (B) Can corpus-based decompounding models be used in the Indian language IR? If yes, how? (C) Can machine learning and deep learning-based decompounding models be applied in the Indian language IR? If yes, how? (D) Among the different decompounding models (corpus-based, hybrid machine learning-based, and deep learning-based), which provides the best effectiveness in the IR domain? (E) Among the different IR models, which provides the best effectiveness from the IR perspective? This study proposes different corpus-based, hybrid machine learning-based, and deep learning-based decompounding models in Indian languages (Marathi, Hindi, and Sanskrit). Moreover, we evaluate the effectiveness of each activity from an IR perspective only. It is observed that the different decompounding models improve IR effectiveness. The deep learning-based decompounding models outperform the corpus-based and hybrid machine learning-based models in Indian language IR. Among the different deep learning-based models, the Bi-LSTM-A model performs best and improves mean average precision (MAP) by 28.02% in Marathi. Similarly, the Bi-RNN-A model improves MAP by 18.18% and 6.1% in Hindi and Sanskrit, respectively. Among the retrieval models, the In_expC2 model outperforms others in Marathi and Hindi, and the BB2 model outperforms others in Sanskrit.

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Section: Corpus-based Decompounding Methodsmentioning

confidence: 99%

A case study on decompounding in Indian language IR

Sahu,

Pal

2024

Nat. lang. processing

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A two-pass approach for handling out-of-vocabulary words in a large vocabulary recognition task

Scharenborg¹,

Seneff²,

Boves³

2007

Computer Speech & Language

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This paper addresses the problem of recognizing a vocabulary of over 50,000 city names in a telephone access spoken dialogue system. We adopt a two-stage framework in which only major cities are represented in the first stage lexicon. We rely on an unknown word model encoded as a phone loop to detect OOV city names (referred to as ‘rare city’ names). We use SpeM, a tool that can extract words and word-initial cohorts from phone graphs from a large fallback lexicon, to provide an N-best list of promising city name hypotheses on the basis of the phone graph corresponding to the OOV. This N-best list is then inserted into the second stage lexicon for a subsequent recognition pass. Experiments were conducted on a set of spontaneous telephone-quality utterances; each containing one rare city name. It appeared that SpeM was able to include nearly 75% of the correct city names in an N-best hypothesis list of 3000 city names. With the names found by SpeM to extend the lexicon of the second stage recognizer, a word accuracy of 77.3% could be obtained. The best one-stage system yielded a word accuracy of 72.6%. The absolute number of correctly recognized rare city names almost doubled, from 62 for the best one-stage system to 102 for the best two-stage system. However, even the best two-stage system recognized only about one-third of the rare city names retrieved by SpeM. The paper discusses ways for improving the overall performance in the context of an application

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N-best: the northern- and southern-dutch benchmark evaluation of speech recognition technology

Kessens

Leeuwen

2007

Interspeech 2007

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A hybrid approach to compounds in LVCSR

Cited by 4 publications

References 4 publications

A case study on decompounding in Indian language IR

A case study on decompounding in Indian language IR

A two-pass approach for handling out-of-vocabulary words in a large vocabulary recognition task

N-best: the northern- and southern-dutch benchmark evaluation of speech recognition technology

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