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arxiv: 1907.08293 · v1 · pith:QFGTDLPRnew · submitted 2019-07-15 · 📡 eess.AS · cs.CL· cs.SD

Investigating Target Set Reduction for End-to-End Speech Recognition of Hindi-English Code-Switching Data

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

Pith reviewed 2026-05-24 21:30 UTC · model grok-4.3

classification 📡 eess.AS cs.CLcs.SD
keywords end-to-end speech recognitioncode-switchingtarget label reductionHindi-EnglishCTCattention-based modelslimited data
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The pith

Reducing the target label set allows reliable training of end-to-end speech recognizers on limited Hindi-English code-switched data.

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

End-to-end speech recognition systems learn target labels directly from speech and therefore need large training corpora. Code-switching between Hindi and English creates two specific difficulties: an expanded output space from the two languages and a shortage of domain-specific data. The paper proposes shrinking the set of target labels as a way to make training feasible under these constraints. The method is tested on CTC-based and attention-based end-to-end networks using a Hindi-English code-switching corpus, with results placed against both full-label E2E systems and hybrid DNN-HMM baselines.

Core claim

By reducing the number of target labels, end-to-end models can be trained reliably on limited code-switched speech data, as demonstrated on CTC-based and attention-based networks for Hindi-English code-switching.

What carries the argument

Target set reduction that shrinks the output vocabulary to address the expanded label space from multiple languages and the lack of large domain-specific corpora.

If this is right

  • E2E ASR systems become trainable on smaller code-switched datasets than previously required.
  • The reduction technique functions for both CTC-based and attention-based architectures.
  • Performance remains comparable to full-label E2E systems and to hybrid DNN-HMM baselines.
  • The approach opens E2E training for other code-switching tasks that lack large domain corpora.

Where Pith is reading between the lines

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

  • The same label-reduction step may extend to additional language pairs in code-switched ASR.
  • Lower label counts could also reduce training time and memory use for E2E models.
  • The method could be combined with data-augmentation strategies to further improve results on small corpora.
  • Direct tests on other E2E variants such as RNN-T would check whether the benefit holds more generally.

Load-bearing premise

Shrinking the target label set mitigates the challenges of expanded output space and limited data without causing loss of critical distinctions or new generalization issues.

What would settle it

If the reduced-target E2E systems produce substantially higher word error rates than the full-target E2E system on the same held-out test data, the benefit of the reduction would be falsified.

Figures

Figures reproduced from arXiv: 1907.08293 by Ganji Sreeram, Kumar Priyadarshi, Kunal Dhawan, Rohit Sinha.

Figure 1
Figure 1. Figure 1: Block diagram of E2E Networks using: a) CTC mechanism, and b) attention mechanism. [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗
read the original abstract

End-to-end (E2E) systems are fast replacing the conventional systems in the domain of automatic speech recognition. As the target labels are learned directly from speech data, the E2E systems need a bigger corpus for effective training. In the context of code-switching task, the E2E systems face two challenges: (i) the expansion of the target set due to multiple languages involved, and (ii) the lack of availability of sufficiently large domain-specific corpus. Towards addressing those challenges, we propose an approach for reducing the number of target labels for reliable training of the E2E systems on limited data. The efficacy of the proposed approach has been demonstrated on two prominent architectures, namely CTC-based and attention-based E2E networks. The experimental validations are performed on a recently created Hindi-English code-switching corpus. For contrast purpose, the results for the full target set based E2E system and a hybrid DNN-HMM system are also reported.

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

0 major / 2 minor

Summary. The paper proposes an approach for reducing the target label set in end-to-end (E2E) ASR systems to address the expanded output vocabulary and limited corpus size challenges in Hindi-English code-switching. Efficacy is demonstrated through experiments on CTC-based and attention-based E2E architectures, with comparisons to full-target E2E systems and hybrid DNN-HMM baselines on a recently created Hindi-English code-switching corpus.

Significance. If the target reduction preserves necessary distinctions and yields competitive or improved accuracy on limited data, the work would be significant for E2E ASR in code-switched and low-resource settings. The evaluation across two architectures plus explicit baselines strengthens the central claim relative to typical single-model reports.

minor comments (2)
  1. [Abstract] Abstract: the claim of demonstrated efficacy would be more informative if key quantitative results (e.g., WER on the code-switching corpus for reduced vs. full target sets) were included rather than left entirely to the body.
  2. The description of the target-reduction procedure itself would benefit from an explicit algorithm or pseudocode block to clarify how labels from the two languages are merged or pruned while retaining phonetic coverage.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive summary, significance assessment, and recommendation of minor revision. No major comments were raised in the report, so we interpret this as an indication that the core contributions and experiments are viewed favorably. We are happy to incorporate any minor suggestions during revision.

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper is an empirical study proposing a target-label reduction method for E2E ASR on Hindi-English code-switching data and validating it via experiments on CTC and attention-based architectures against full-target E2E and hybrid DNN-HMM baselines on the given corpus. No derivation chain, equations, fitted parameters renamed as predictions, or self-citation load-bearing steps are present in the abstract or described claim structure; the efficacy claim rests on direct experimental comparisons rather than any self-referential reduction to inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Abstract-only review; no explicit free parameters, invented entities, or non-standard axioms are stated. Background assumptions are limited to standard domain knowledge about E2E training data needs.

axioms (1)
  • domain assumption End-to-end ASR systems require larger corpora than conventional systems for effective training.
    Stated as background fact in the first sentence of the abstract.

pith-pipeline@v0.9.0 · 5713 in / 1320 out tokens · 28577 ms · 2026-05-24T21:30:55.735375+00:00 · methodology

discussion (0)

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Reference graph

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