Adapting monolingual resources for code-mixed hindi-english speech recognition

Pandey, Ayushi; Srivastava, Brij Mohan Lai; Gangashetty, Suryakanth V.

doi:10.1109/ialp.2017.8300583

Cited by 12 publications

(7 citation statements)

References 7 publications

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“…The vast majority of the available CS speech corpora have covered Chinese-English [28,86,66,70,2], Hindi-English [34,82,87,90,78] and Spanish-English [88,33,82] language pairs. Less work has covered Arabic-English [54,46,47], Arabic-French [12,14], Frisian-Dutch [101], Mandarin-Taiwanese [69,68], Turkish-German [26], English-Malay [2], English-isiZulu [98] and Sepedi-English [73].…”

Section: Cs Speech Corporamentioning

confidence: 99%

Investigations on Speech Recognition Systems for Low-Resource Dialectal Arabic-English Code-Switching Speech

Hamed¹,

Denisov²,

Li³

et al. 2021

Preprint

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Code-switching (CS), defined as the mixing of languages in conversations, has become a worldwide phenomenon. The prevalence of CS has been recently met with a growing demand and interest to build CS ASR systems. In this paper, we present our work on code-switched Egyptian Arabic-English automatic speech recognition (ASR). We first contribute in filling the huge gap in resources by collecting, analyzing and publishing our spontaneous CS Egyptian Arabic-English speech corpus. We build our ASR systems using DNN-based hybrid and Transformer-based end-to-end models. In this paper, we present a thorough comparison between both approaches under the setting of a low-resource, orthographically unstandardized, and morphologically rich language pair. We show that while both systems give comparable overall recognition results, each system provides complementary sets of strength points. We show that recognition can be improved by combining the outputs of both systems. We propose several effective system combination approaches, where hypotheses of both systems are merged on sentence-and word-levels. Our approaches result in overall WER relative improvement of 4.7%, over a baseline performance of 32.1% WER. In the case of intra-sentential CS sentences, we achieve WER relative improvement of 4.8%. Our best performing system achieves 30.6% WER on ArzEn test set.

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Section: Cs Speech Corporamentioning

confidence: 99%

Investigations on Speech Recognition Systems for Low-Resource Dialectal Arabic-English Code-Switching Speech

Hamed¹,

Denisov²,

Li³

et al. 2021

Preprint

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show abstract

“…• [44] describe the creation of a phonetically balanced Hindi-English corpus for code-switched ASR. This corpus contains read speech from 78 speakers, with each speaker having recorded around a minute of speech.…”

Section: Speech Datamentioning

confidence: 99%

A Survey of Code-switched Speech and Language Processing

Sitaram¹,

Chandu²,

Rallabandi³

et al. 2019

Preprint

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Code-switching, the alternation of languages within a conversation or utterance, is a common communicative phenomenon that occurs in multilingual communities across the world. This survey reviews computational approaches for codeswitched Speech and Natural Language Processing. We motivate why processing code-switched text and speech is essential for building intelligent agents and systems that interact with users in multilingual communities. As code-switching data and resources are scarce, we list what is available in various code-switched language pairs with the language processing tasks they can be used for. We review code-switching research in various Speech and NLP applications, including language processing tools and end-to-end systems. We conclude with future directions and open problems in the field.

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“…Yeh et al (Yeh and Lee, 2015) tackle the problem of code-switching in which a speaker speaks mainly in one language, leading to an imbalance in the amount of data available in the two languages, with cross-lingual data sharing approaches. (Pandey et al, 2017) also propose studies to adapt matrix language (monolingual Hindi) resource to build better code-mixed acoustic model in case of read speech.…”

Section: Relation To Prior Workmentioning

confidence: 99%

Phone Merging For Code-Switched Speech Recognition

Sivasankaran¹,

Srivastava

Sitaram

et al. 2018

Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching

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Speakers in multilingual communities often switch between or mix multiple languages in the same conversation. Automatic Speech Recognition (ASR) of codeswitched speech faces many challenges including the influence of phones of different languages on each other. This paper shows evidence that phone sharing between languages improves the Acoustic Model performance for Hindi-English code-switched speech. We compare baseline system built with separate phones for Hindi and English with systems where the phones were manually merged based on linguistic knowledge. Encouraged by the improved ASR performance after manually merging the phones, we further investigate multiple data-driven methods to identify phones to be merged across the languages. We show detailed analysis of automatic phone merging in this language pair and the impact it has on individual phone accuracies and WER. Though the best performance gain of 1.2% WER was observed with manually merged phones, we show experimentally that the manual phone merge

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Adapting monolingual resources for code-mixed hindi-english speech recognition

Cited by 12 publications

References 7 publications

Investigations on Speech Recognition Systems for Low-Resource Dialectal Arabic-English Code-Switching Speech

Investigations on Speech Recognition Systems for Low-Resource Dialectal Arabic-English Code-Switching Speech

A Survey of Code-switched Speech and Language Processing

Phone Merging For Code-Switched Speech Recognition

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