pith. sign in

arxiv: 1907.06342 · v1 · pith:5L24MNGWnew · submitted 2019-07-15 · 💻 cs.CL · cs.SD· eess.AS

Joint Language Identification of Code-Switching Speech using Attention based E2E Network

Sreeram Ganji , Kunal Dhawan , Kumar Priyadarshi , Rohit Sinha This is my paper

Pith reviewed 2026-05-24 21:50 UTC · model grok-4.3

classification 💻 cs.CL cs.SDeess.AS
keywords language identificationcode-switchingattention mechanismend-to-end networkHindi-English corpusjoint modelingspeech processing
0
0 comments X

The pith

An attention-based end-to-end network jointly identifies languages in code-switched speech and locates switch points via attention weights.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper proposes modeling the languages inside a code-switched utterance jointly inside one attention-based end-to-end network rather than building separate models for each language. The system is developed and tested on a Hindi-English code-switching corpus and is compared against a connectionist temporal classification end-to-end network. The attention approach yields higher language identification accuracy and the plotted attention weights show where language switches occur inside an utterance.

Core claim

An attention-based end-to-end network that jointly models the languages present in code-switching speech achieves better language identification accuracy than a connectionist temporal classification end-to-end network on a Hindi-English corpus, and the attention weights of the network mark the locations of language switches inside utterances.

What carries the argument

Attention-based end-to-end network performing joint language modeling of the languages inside a single network.

If this is right

  • Joint language modeling inside one network is feasible for code-switching speech.
  • Attention weights inside the end-to-end network mark language boundaries inside utterances.
  • The attention approach outperforms the CTC-based end-to-end baseline on the Hindi-English corpus.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same joint network could feed directly into a downstream code-switched speech recognizer without an explicit language detector.
  • Attention plots could serve as a diagnostic tool for spotting language transition patterns in new corpora.
  • The joint-modeling idea might extend to three or more languages inside one utterance if the network capacity scales.

Load-bearing premise

Joint modelling of the underlying languages inside a single attention-based E2E network is feasible and superior to separate modelling of each language.

What would settle it

On a held-out code-switching test set the attention-based system shows equal or lower accuracy than the CTC-based system, or the attention weights fail to align with actual language switch points.

Figures

Figures reproduced from arXiv: 1907.06342 by Kumar Priyadarshi, Kunal Dhawan, Rohit Sinha, Sreeram Ganji.

Figure 1
Figure 1. Figure 1: Architecture of the CTC-based E2E network. The encoder is a deep network consisting of [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Architecture of LAS network. It consists of three modules namely: listener (encoder), attender [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Creation of character-level LID tags for the training data towards conditioning the E2E networks [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Visualization of attention mechanism for LID task. For a given Hindi-English code-switching [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
read the original abstract

Language identification (LID) has relevance in many speech processing applications. For the automatic recognition of code-switching speech, the conventional approaches often employ an LID system for detecting the languages present within an utterance. In the existing works, the LID on code-switching speech involves modelling of the underlying languages separately. In this work, we propose a joint modelling based LID system for code-switching speech. To achieve the same, an attention-based end-to-end (E2E) network has been explored. For the development and evaluation of the proposed approach, a recently created Hindi-English code-switching corpus has been used. For the contrast purpose, an LID system employing the connectionist temporal classification-based E2E network is also developed. On comparing both the LID systems, the attention based approach is noted to result in better LID accuracy. The effective location of code-switching boundaries within the utterance by the proposed approach has been demonstrated by plotting the attention weights of E2E network.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 1 minor

Summary. The paper proposes a joint language identification (LID) system for code-switching speech using an attention-based end-to-end (E2E) network, motivated by the observation that prior work models the underlying languages separately. It develops and evaluates the approach on a Hindi-English code-switching corpus, contrasts it with a CTC-based E2E network (also joint), reports superior LID accuracy for the attention model, and demonstrates boundary localization by visualizing attention weights.

Significance. If the empirical comparison holds after addressing the comparison gap, the work would provide evidence that attention-based joint E2E modelling can outperform CTC-based joint modelling for LID while offering interpretable boundary detection; this could inform future code-switching speech systems, though the absence of a direct test against separate-modelling baselines reduces the ability to substantiate the joint-modelling motivation.

major comments (1)
  1. [Abstract] Abstract (and §1): The central motivation states that 'existing works... involve modelling of the underlying languages separately' and positions the contribution as 'joint modelling based LID system,' yet the only reported comparison is between the proposed attention E2E and a CTC-based E2E network; both are joint models, so the experiment does not test whether joint modelling inside one network is feasible or superior to the conventional separate approach that motivates the paper.
minor comments (1)
  1. [Abstract] Abstract: No quantitative accuracy figures, error bars, dataset statistics, or baseline details are supplied, which weakens the ability to evaluate the 'better LID accuracy' claim without consulting the results section.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive feedback. We address the major comment point-by-point below and propose targeted revisions to improve clarity without altering the core experiments.

read point-by-point responses

  1. Referee: [Abstract] Abstract (and §1): The central motivation states that 'existing works... involve modelling of the underlying languages separately' and positions the contribution as 'joint modelling based LID system,' yet the only reported comparison is between the proposed attention E2E and a CTC-based E2E network; both are joint models, so the experiment does not test whether joint modelling inside one network is feasible or superior to the conventional separate approach that motivates the paper.

    Authors: We acknowledge the observation. The manuscript's motivation correctly notes that prior LID work on code-switching typically models languages separately, and our contribution is a joint E2E architecture. The reported experiments compare two joint implementations (attention vs. CTC) to isolate the effect of the attention mechanism on LID accuracy and boundary localization. This design demonstrates that joint modelling is feasible and effective within a single network, but does not include a head-to-head evaluation against separate-modelling baselines. In revision we will (i) rephrase the abstract and §1 to state that the work shows joint modelling is viable rather than claiming superiority over separate approaches, and (ii) add a limitations paragraph noting the absence of such a baseline comparison as future work. No new experiments will be added. revision: partial

Circularity Check

0 steps flagged

No circularity; empirical architecture comparison on external corpus

full rationale

The paper reports an empirical comparison of two joint E2E LID architectures (attention vs. CTC) on a Hindi-English code-switching corpus. No mathematical derivations, fitted parameters renamed as predictions, self-definitional quantities, or load-bearing self-citations appear in the provided text. The central result is an accuracy delta between two independently trained networks, which does not reduce to its own inputs by construction. The noted mismatch between stated motivation (joint vs. separate modelling) and actual experiment is a design limitation, not circularity.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The paper rests on standard assumptions of deep learning for sequence tasks and the representativeness of the cited Hindi-English corpus; no new entities are introduced and hyperparameters are not enumerated in the abstract.

free parameters (1)
  • E2E network hyperparameters
    Typical training choices such as learning rate and attention configuration are required for any E2E model but are not specified.
axioms (1)
  • domain assumption Joint modelling inside one network is feasible and preferable to separate per-language modelling for code-switching LID
    This premise is invoked to motivate the proposed attention-based system over conventional approaches.

pith-pipeline@v0.9.0 · 5711 in / 1175 out tokens · 26292 ms · 2026-05-24T21:50:37.772948+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

31 extracted references · 31 canonical work pages · 2 internal anchors

  1. [1]

    John J Gumperz, Discourse Strategies, Cambridge University Press, 1982

    work page 1982

  2. [2]

    Codeswitching as an urban language-contact phenomenon,

    Carol M Eastman, “Codeswitching as an urban language-contact phenomenon,” Journal of Multilingual & Multicultural Development , vol. 13, no. 1-2, pp. 1–17, 1992

    work page 1992

  3. [3]

    Comparing codeswitching and borrowing,

    Carol Myers Scotton, “Comparing codeswitching and borrowing,” Journal of Multilingual & Multicul- tural Development, vol. 13, no. 1-2, pp. 19–39, 1992

    work page 1992

  4. [4]

    I am borrowing ya mixing? An Analysis of English-Hindi Code Mixing in Facebook,

    Kalika Bali, Jatin Sharma, Monojit Choudhury, and Yogarshi Vyas, “I am borrowing ya mixing? An Analysis of English-Hindi Code Mixing in Facebook,” in Proc. of the First Workshop on Computational Approaches to Code Switching , 2014, pp. 116–126

    work page 2014

  5. [5]

    Code-mixing in social media text: The last language identification frontier?,

    Amitava Das and Bj¨ orn Gamb¨ ack, “Code-mixing in social media text: The last language identification frontier?,” in Proc.of Traitement Automatique des Langues (ATALA) , 2015

    work page 2015

  6. [6]

    LTD., 1994

    Lalita Malik, Socio-linguistics: A study of code-switching , Anmol Publications PVT. LTD., 1994

    work page 1994

  7. [7]

    Code-switching between Mandarin and Taiwanese in three telephone conversation: The negotiation of interpersonal relationships among bilingual speakers in Taiwan,

    Hsi-Yao Su, “Code-switching between Mandarin and Taiwanese in three telephone conversation: The negotiation of interpersonal relationships among bilingual speakers in Taiwan,” in Proc. of the Sympo- sium about Language and Society , 2001

    work page 2001

  8. [8]

    Building a First Language Model for Code- switch Arabic-English,

    Injy Hamed, Mohamed Elmahdy, and Slim Abdennadher, “Building a First Language Model for Code- switch Arabic-English,” Procedia Computer Science, vol. 117, pp. 208–216, 2017

    work page 2017

  9. [9]

    The French-Algerian code-switching triggered audio corpus (FACST).,

    Djegdjiga Amazouz, Martine Adda-Decker, and Lori Lamel, “The French-Algerian code-switching triggered audio corpus (FACST).,” inProc. of Language Resources and Evaluation Conference (LREC) , 2018

    work page 2018

  10. [10]

    MediaParl: Bilingual mixed language accented speech database,

    David Imseng, Herv´ e Bourlard, Holger Caesar, Philip N Garner, Gw´ enol´ e Lecorv´ e, and Alexandre 11 Nanchen, “MediaParl: Bilingual mixed language accented speech database,” in Proc. of Spoken Language Technology Workshop (SLT) , 2012, pp. 263–268

    work page 2012

  11. [11]

    A longitudinal bilingual Frisian-Dutch radio broadcast database designed for code-switching research,

    Emre Yilmaz, Maaike Andringa, Sigrid Kingma, Jelske Dijkstra, Frits Van der Kuip, Hans Van de Velde, Frederik Kampstra, Jouke Algra, H Heuvel, and David Van Leeuwen, “A longitudinal bilingual Frisian-Dutch radio broadcast database designed for code-switching research,” in Proceedings of the International Conference on Language Resources and Evaluation (LR...

    work page 2016

  12. [12]

    Hindi-English, Code Switching and Language Choice in Urban, Uppermiddle-class Indian Families,

    Sunita Malhotra, “Hindi-English, Code Switching and Language Choice in Urban, Uppermiddle-class Indian Families,” University of Kansas. Linguistics Graduate Student Association , 1980

    work page 1980

  13. [13]

    A Hindi-English Code-Switching Corpus.,

    Anik Dey and Pascale Fung, “A Hindi-English Code-Switching Corpus.,” in Proc. of the Language Resources and Evaluation Conference (LREC) , 2014, pp. 2410–2413

    work page 2014

  14. [14]

    Automatic speech recognition of code switching speech using 1-best rescoring,

    Basem HA Ahmed and Tien-Ping Tan, “Automatic speech recognition of code switching speech using 1-best rescoring,” in Proc. of International Conference on Asian Language Processing (IALP) , 2012, pp. 137–140

    work page 2012

  15. [15]

    SEAME: A Mandarin-English code-switching speech corpus in South-East Asia,

    Dau-Cheng Lyu, Tien-Ping Tan, Eng Siong Chng, and Haizhou Li, “SEAME: A Mandarin-English code-switching speech corpus in South-East Asia,” in Proc. of Interspeech, an Annual Conference of International Speech Communication Association , 2010

    work page 2010

  16. [16]

    Speech recognition on code-switching among the Chinese dialects,

    Dau Cheng Lyu, Ren Yuan Lyu, Yuang Chin Chiang, and Chun Nan Hsu, “Speech recognition on code-switching among the Chinese dialects,” in Proc. of International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2006, vol. 1

    work page 2006

  17. [17]

    Part-of-Speech tagging for English-Spanish code-switched text,

    Thamar Solorio and Yang Liu, “Part-of-Speech tagging for English-Spanish code-switched text,” in Proc. of the Conference on Empirical Methods in Natural Language Processing . Association for Com- putational Linguistics, 2008, pp. 1051–1060

    work page 2008

  18. [18]

    Mixed language speech recognition without explicit identification of language,

    Kiran Bhuvanagirir and Sunil Kumar Kopparapu, “Mixed language speech recognition without explicit identification of language,” American Journal of Signal Processing , vol. 2, no. 5, pp. 92–97, 2012

    work page 2012

  19. [19]

    Language identification on code-switching utterances using mul- tiple cues,

    Dau Cheng Lyu and Ren Yuan Lyu, “Language identification on code-switching utterances using mul- tiple cues,” in Proc. of Interspeech, an Annual Conference of the International Speech Communication Association, 2008

    work page 2008

  20. [20]

    Semantics-based language modeling for Cantonese-English code-mixing speech recognition,

    Houwei Cao, PC Ching, Tan Lee, and Yu Ting Yeung, “Semantics-based language modeling for Cantonese-English code-mixing speech recognition,” in Proc. of 7th International Symposium on Chi- nese Spoken Language Processing (ISCSLP) , 2010, pp. 246–250

    work page 2010

  21. [21]

    An integrated framework for transcribing Mandarin-English code-mixed lectures with improved acoustic and language modeling,

    Ching Feng Yeh, Chao Yu Huang, Liang Che Sun, Che Liang, and Lin Shan Lee, “An integrated framework for transcribing Mandarin-English code-mixed lectures with improved acoustic and language modeling,” in Proc. of 7th International Symposium on Chinese Spoken Language Processing (ISCSLP) , 2010, pp. 214–219. 12

    work page 2010

  22. [22]

    Speech Synthesis of Code-Mixed Text.,

    Sunayana Sitaram and Alan W Black, “Speech Synthesis of Code-Mixed Text.,” in Proc. of Language Resources and Evaluation Conference LREC , 2016

    work page 2016

  23. [23]

    Hindi-English Code-Switching Speech Corpus

    Ganji Sreeram, Kunal Dhawan, and Rohit Sinha, “Hindi-English code-switching speech corpus,” arXiv:1810.00662, 2018

    work page internal anchor Pith review Pith/arXiv arXiv 2018

  24. [24]

    Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural networks,

    Alex Graves, Santiago Fern´ andez, Faustino Gomez, and J¨ urgen Schmidhuber, “Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural networks,” in Proc. of the 23rd International Conference on Machine learning , 2006, pp. 369–376

    work page 2006

  25. [25]

    End-to-end continuous speech recognition using attention-based recurrent NN: First results,

    Jan Chorowski, Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio, “End-to-end continuous speech recognition using attention-based recurrent NN: First results,” in Proc. of Deep Learning and Representation Learning Workshop, 2014

    work page 2014

  26. [26]

    End-to-end language identification using attention-based recurrent neural networks,

    Wang Geng, Wenfu Wang, Yuanyuan Zhao, Xinyuan Cai, Bo Xu, Cai Xinyuan, et al., “End-to-end language identification using attention-based recurrent neural networks,” in Proc. of Interspeech, an Annual Conference of International Speech Communication Association , 2016

    work page 2016

  27. [27]

    Intrasentential vs. intersentential code switching in early and late bilinguals,

    Kelly Ann Hill Zirker, “Intrasentential vs. intersentential code switching in early and late bilinguals,” 2007

    work page 2007

  28. [28]

    Listen, attend and spell: A neural network for large vocabulary conversational speech recognition,

    William Chan, Navdeep Jaitly, Quoc Le, and Oriol Vinyals, “Listen, attend and spell: A neural network for large vocabulary conversational speech recognition,” in Proc. of International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2016, pp. 4960–4964

    work page 2016

  29. [29]

    Nabu: An end-to-end speech recognition toolkit,

    Vincent, “Nabu: An end-to-end speech recognition toolkit,” [Online] https://vrenkens.github.io/ nabu/, Accessed: 2019-03-24

    work page 2019

  30. [30]

    Voting algorithms,

    Behrooz Parhami, “Voting algorithms,” IEEE Transactions on Reliability , vol. 43, no. 4, pp. 617–629, 1994

    work page 1994

  31. [31]

    Towards End-to-End Code-Switching Speech Recognition

    Ne Luo, Dongwei Jiang, Shuaijiang Zhao, Caixia Gong, Wei Zou, and Xiangang Li, “Towards end-to- end code-switching speech recognition,” arXiv preprint arXiv:1810.13091 , 2018. 13

    work page internal anchor Pith review Pith/arXiv arXiv 2018