Unsupervised Language Model Adaptation for Speech Recognition with no Extra Resources

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F19%3APU134188" target="_blank" >RIV/00216305:26230/19:PU134188 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.dega-akustik.de/publikationen/online-proceedings/" target="_blank" >https://www.dega-akustik.de/publikationen/online-proceedings/</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Unsupervised Language Model Adaptation for Speech Recognition with no Extra Resources

Popis výsledku v původním jazyce

Classically, automatic speech recognition (ASR) models are decomposed into acoustic models and language models (LM). LMs usually exploit the linguistic structure on a purely textual level and usually contribute strongly to an ASR systems performance. LMs are estimated on large amounts of textual data covering the target domain. However, most utterances cover more specic topics, e.g. in uencing the vocabulary used. Therefore, it's desirable to have the LM adjusted to an utterance's topic. Previous work achieves this by crawling extra data from the web or by using signicant amounts of previous speech data to train topic-specic LM on. We propose a way of adapting the LM directly using the target utterance to be recognized. The corresponding adaptation needs to be done in an unsupervised or automatically supervised way based on the speech input. To deal with corresponding errors robustly, we employ topic encodings from the recently proposed Subspace Multinomial Model. This model also avoids any need of explicit topic labelling during training or recognition, making the proposed method straight-forward to use. We demonstrate the performance of the method on the Librispeech corpus, which consists of read ction books, and we discuss it's behaviour qualitatively.

Název v anglickém jazyce

Unsupervised Language Model Adaptation for Speech Recognition with no Extra Resources

Popis výsledku anglicky

Classically, automatic speech recognition (ASR) models are decomposed into acoustic models and language models (LM). LMs usually exploit the linguistic structure on a purely textual level and usually contribute strongly to an ASR systems performance. LMs are estimated on large amounts of textual data covering the target domain. However, most utterances cover more specic topics, e.g. in uencing the vocabulary used. Therefore, it's desirable to have the LM adjusted to an utterance's topic. Previous work achieves this by crawling extra data from the web or by using signicant amounts of previous speech data to train topic-specic LM on. We propose a way of adapting the LM directly using the target utterance to be recognized. The corresponding adaptation needs to be done in an unsupervised or automatically supervised way based on the speech input. To deal with corresponding errors robustly, we employ topic encodings from the recently proposed Subspace Multinomial Model. This model also avoids any need of explicit topic labelling during training or recognition, making the proposed method straight-forward to use. We demonstrate the performance of the method on the Librispeech corpus, which consists of read ction books, and we discuss it's behaviour qualitatively.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/EF16_027%2F0008371" target="_blank" >EF16_027/0008371: Mezinárodní mobilita výzkumníků Vysokého učení technického v Brně</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of DAGA 2019

ISBN

978-3-939296-14-0

ISSN

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e-ISSN

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Počet stran výsledku

4

Strana od-do

954-957

Název nakladatele

DEGA Head office, Deutsche Gesellschaft für Akustik

Místo vydání

Rostock

Místo konání akce

Rostock

Datum konání akce

18. 3. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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