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Influence of Accented Speech in Automatic Speech Recognition: A Case Study on Assamese L1 Speakers Speaking Code Switched Hindi-English

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Speech and Computer (SPECOM 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13721))

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Abstract

The goal of this work is to show the influence of accented speech on state-of-the-art speech-to-text (S2T) systems. In the current study, Assamese accented Hindi-English (AAHE) code-switched speech samples and Native Hindi-English (NHE) code-switched speech samples are subjected to four commercial S2T systems. The results of this study found that the word error rate averaged across the four systems is found to be 27.33% and 38.35% for the NHE and AAHE groups, respectively. This performance gap is mainly attributed to substitution errors. On further analysis, it was found that those errors resulted from the distinct phonetic and phonological properties of the Assamese language. Thus, there is a scope for accent adaptation even in concurrent S2T systems supporting Indian languages.

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Notes

  1. 1.

    https://sites.google.com/view/testoffluency/home.

  2. 2.

    http://progfruits.blogspot.com/2014/02/word-error-rate-wer-and-word.html.

  3. 3.

    https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-models-ng.

References

  1. Amazon Transcribe: Speech to Text - AWS. https://aws.amazon.com/transcribe. Accessed 14 Mar 2022

  2. Bates, D., Mächler, M., Bolker, B., Walker, S.: Fitting linear mixed-effects models using LME4. J. Stat. Softw. 67(1), 1–48 (2015). https://doi.org/10.18637/jss.v067.i01

    Article  Google Scholar 

  3. Diwan, A., et al.: Multilingual and code-switching ASR challenges for low resource Indian languages. In: Proceedings of Interspeech (2021)

    Google Scholar 

  4. Google Cloud: Speech-to-Text: Automatic Speech Recognition. https://cloud.google.com/speech-to-text. Accessed 04 Mar 2022

  5. IBM Watson: Watson Speech to Text. https://www.ibm.com/cloud/watson-speech-to-text. Accessed 09 Mar 2022

  6. Mennen, I.: Phonological and phonetic influences in non-native intonation. Trends Linguist. Stud. Monogr. 186, 53 (2007)

    Google Scholar 

  7. Microsoft Azure: Speech-to-Text. https://azure.microsoft.com/en-us/services/cognitive-services/speech-to-text. Accessed 01 Mar 2022

  8. Mishra, S., Mishra, A.: Linguistic Interference from Hindi in Indian English. Int. J. Stud. Engl. Langu. Lit. 4(1), 29–38 (2016)

    Google Scholar 

  9. Office of the Registrar & General Census Commissioner, India: C-17 population by bilingualism and trilingualism (2011). https://censusindia.gov.in/2011census/C-17.html. Accessed 24 Mar 2022

  10. Office of the Registrar General & Census Commissioner, India: Family-wise grouping of scheduled and non-scheduled languages (2011). https://censusindia.gov.in/2011Census/Language-2011/Statement-9.pdf. Accessed 24 Mar 2022

  11. R Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2019). https://www.R-project.org/

  12. Rasier, L., Hiligsmann, P.: Prosodic transfer from L1 to L2. Theoretical and methodological issues. Nouveaux cahiers de linguistique française (New Noteb. French Linguist.) 28, 41–66 (2007)

    Google Scholar 

  13. Seide, F., Li, G., Yu, D.: Conversational speech transcription using context-dependent deep neural networks. In: Proceedings of Twelfth Annual Conference of the International Speech Communication Association (2011)

    Google Scholar 

  14. Tomokiyo, L.M.: Lexical and acoustic modeling of non-native speech in LVSCR. In: Proceedings of Sixth International Conference on Spoken Language Processing (2000)

    Google Scholar 

  15. Vu, N.T., Wang, Y., Klose, M., Mihaylova, Z., Schultz, T.: Improving ASR performance on non-native speech using multilingual and crosslingual information. In: Proceedings of Fifteenth Annual Conference of the International Speech Communication Association (2014)

    Google Scholar 

  16. Wiltshire, C., Sarmah, P.: Voicing contrasts in the stops of Indian English produced by Assamese speakers. Proc. Meet. Acoust. 42(1), 060003 (2020)

    Google Scholar 

  17. Wiltshire, C.R.: The “Indian English’’ of Tibeto-Burman language speakers. Engl. World Wide 26(3), 275–300 (2005)

    Article  Google Scholar 

  18. Wiltshire, C.R.: Uniformity and Variability in the Indian English Accent. Elements in World Englishes. Cambridge University Press (2020). https://doi.org/10.1017/9781108913768

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Authors and Affiliations

  1. Centre for Linguistic Science and Technology, Indian Institute of Technology Guwahati, Guwahati, 781039, India

    Joyshree Chakraborty, Rohit Sinha & Priyankoo Sarmah

Authors
  1. Joyshree Chakraborty
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  2. Rohit Sinha
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  3. Priyankoo Sarmah
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Corresponding author

Correspondence to Joyshree Chakraborty .

Editor information

Editors and Affiliations

  1. Indian Institute of Technology Dharwad, Dharwad, India

    S. R. Mahadeva Prasanna

  2. St. Petersburg Federal Research Center of the Russian Academy of Sciences, St. Petersburg, Russia

    Alexey Karpov

  3. Koneru Lakshmaiah Education Foundation, Vaddeswaram, India

    K. Samudravijaya

  4. KIIT Group of Colleges, Gurugram, India

    Shyam S. Agrawal

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Chakraborty, J., Sinha, R., Sarmah, P. (2022). Influence of Accented Speech in Automatic Speech Recognition: A Case Study on Assamese L1 Speakers Speaking Code Switched Hindi-English. In: Prasanna, S.R.M., Karpov, A., Samudravijaya, K., Agrawal, S.S. (eds) Speech and Computer. SPECOM 2022. Lecture Notes in Computer Science(), vol 13721. Springer, Cham. https://doi.org/10.1007/978-3-031-20980-2_9

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  • DOI: https://doi.org/10.1007/978-3-031-20980-2_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20979-6

  • Online ISBN: 978-3-031-20980-2

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