Improving Performance of End-to-End ASR on Numeric Sequences

Cal Peyser; Hao Zhang; Tara N. Sainath; Zelin Wu

arxiv: 1907.01372 · v1 · pith:CPLGYHLQnew · submitted 2019-07-01 · 📡 eess.AS · cs.LG· cs.SD· stat.ML

Improving Performance of End-to-End ASR on Numeric Sequences

Cal Peyser , Hao Zhang , Tara N. Sainath , Zelin Wu This is my paper

Pith reviewed 2026-05-25 11:27 UTC · model grok-4.3

classification 📡 eess.AS cs.LGcs.SDstat.ML

keywords end-to-end ASRnumeric sequencesTTS data augmentationneural denormalizationon-device speech recognitionword error ratespoken-to-written conversion

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The pith

End-to-end ASR models reduce word error rates on long numeric sequences by up to a factor of eight by augmenting training with TTS data and replacing large FST denormalizers with a small neural network.

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

End-to-end ASR models face an out-of-vocabulary problem when users speak numbers in written form, such as prices or phone numbers, because conventional training leaves those sequences unseen. Traditional systems solve this by training on spoken forms and then applying a large finite-state transducer to convert back to written form, but that transducer uses too much memory for on-device deployment. The paper demonstrates that generating extra numeric examples with text-to-speech and training a compact neural network to perform the spoken-to-written conversion improves accuracy on multiple numeric categories. The gains are largest on the longest sequences, where error rates fall by as much as eight times. Readers care because the method keeps the entire system inside the low-memory envelope required for phones and watches.

Core claim

Recognizing written-domain numeric utterances remains difficult for end-to-end models when numeric sequences are absent from training. Conventional pipelines address the issue by training on spoken-domain data and applying an FST verbalizer for denormalization, yet the verbalizer's memory footprint precludes its use in the on-device setting. Generating additional numeric training data with a text-to-speech system and substituting a small-footprint neural network for the FST verbalizer produces measurable gains across several numeric classes. The largest improvement occurs on the longest numeric sequences, where word error rate falls by up to a factor of eight.

What carries the argument

A small-footprint neural network trained to map spoken-domain numeric output to written-domain form, used together with TTS-generated numeric utterances to augment the training set.

If this is right

Recognition accuracy improves on several distinct numeric classes such as prices, phone numbers, and dates.
Word error rate on the longest numeric sequences drops by as much as a factor of eight.
The entire pipeline remains compatible with the strict memory limits of on-device ASR because the neural denormalizer replaces the large FST component.
End-to-end models can now be trained to handle out-of-vocabulary numeric material without relying on external spoken-domain training pipelines.

Where Pith is reading between the lines

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

The same TTS-plus-neural-denormalizer pattern could be applied to other categories of rare tokens, such as proper names or technical terms, that also suffer from domain mismatch.
Placing the neural denormalizer inside the end-to-end model itself rather than as a post-processing step might further reduce latency on resource-constrained devices.
Measuring how performance changes when the TTS voices are drawn from a wider range of accents would test whether the current gains hold under more varied real-world conditions.

Load-bearing premise

The distribution of numeric utterances produced by the text-to-speech system is close enough to real user speech that models trained on the synthetic data will generalize to actual spoken input.

What would settle it

Evaluating the augmented model on a large set of real-user numeric utterances recorded in the target acoustic conditions and finding no reduction, or an increase, in word error rate relative to the unaugmented baseline would falsify the central claim.

Figures

Figures reproduced from arXiv: 1907.01372 by Cal Peyser, Hao Zhang, Tara N. Sainath, Zelin Wu.

**Figure 1.** Figure 1: Neural Denormer Architecture. T stands for trivial. N stands for non-trivial. S stands for start. C stands for continuation. 2.2.2. Tagging Layer We define the “tagger” RNN as si = RNNtag(si−1, ti−1, hi) where s = si, . . . , sI are hidden tagger states, with corresponding observations, i.e., tag sequence t = ti, . . . , tI . Each tag ti is a joined tag in the cross-product set of {trivial, non-trivial} … view at source ↗

read the original abstract

Recognizing written domain numeric utterances (e.g. I need $1.25.) can be challenging for ASR systems, particularly when numeric sequences are not seen during training. This out-of-vocabulary (OOV) issue is addressed in conventional ASR systems by training part of the model on spoken domain utterances (e.g. I need one dollar and twenty five cents.), for which numeric sequences are composed of in-vocabulary numbers, and then using an FST verbalizer to denormalize the result. Unfortunately, conventional ASR models are not suitable for the low memory setting of on-device speech recognition. E2E models such as RNN-T are attractive for on-device ASR, as they fold the AM, PM and LM of a conventional model into one neural network. However, in the on-device setting the large memory footprint of an FST denormer makes spoken domain training more difficult. In this paper, we investigate techniques to improve E2E model performance on numeric data. We find that using a text-to-speech system to generate additional numeric training data, as well as using a small-footprint neural network to perform spoken-to-written domain denorming, yields improvement in several numeric classes. In the case of the longest numeric sequences, we see reduction of WER by up to a factor of 8.

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.

Desk Editor's Note private letter to a colleague

TTS data plus a compact neural denormer gives claimed WER drops up to 8x on long numeric sequences in E2E ASR, but the abstract supplies almost no controls or comparisons to judge whether the gains are real.

read the letter

The paper's core result is that TTS-augmented training plus a small neural denormer improves E2E ASR on numeric sequences, with up to 8x WER reduction on the longest ones. That is the main thing to know. What stands out is the focus on on-device constraints. They avoid the large FST by using a compact NN for denorming and generate extra numeric data via TTS to handle OOV cases. This is a straightforward adaptation of data augmentation and model compression ideas to the numeric problem in RNN-T models. The paper does a good job laying out why the conventional FST approach doesn't work for low-memory settings and why E2E needs something different. The approach makes sense for the setting. Conventional systems use spoken domain training and FST, but that doesn't fit low-memory on-device E2E. So swapping in TTS data and a neural component is a reasonable engineering move. It targets a frequent real-world case where numbers appear in written form but training data has them spoken out. The soft spot is the lack of supporting details. The abstract mentions WER reductions but gives no baselines, no model sizes, no error bars, and no description of how the TTS data was generated or tested against real speech. Without that, it's difficult to tell if the gains come from the method or from a mismatch between TTS output and actual user utterances. The stress-test note about distribution shift seems on point here; nothing counters the possibility that the TTS data is cleaner or less variable than real numeric speech. If the full paper has those controls, that would change the picture, but based on the summary it's a concern. This paper is aimed at practitioners working on mobile or embedded ASR who deal with numbers a lot. A reader in that area might pick up the two techniques and try them, but anyone looking for a general advance in ASR or new theory will not find much. I would send it to peer review if the full version includes proper ablations and real-data tests, because the problem is real and the fixes are targeted. Otherwise it risks being too thin on evidence. The work shows clear thinking about the constraints of on-device models.

Referee Report

2 major / 1 minor

Summary. The paper claims that for on-device E2E ASR (e.g., RNN-T), augmenting training with TTS-generated numeric utterances and replacing a large FST verbalizer with a small-footprint neural denormalizer improves recognition of written-domain numeric sequences, yielding WER reductions of up to a factor of 8 on the longest numeric classes.

Significance. If the reported gains prove robust, the approach would be significant for memory-constrained on-device ASR by addressing numeric OOV without relying on large FST components; the combination of data augmentation and compact denorming directly targets a practical deployment constraint.

major comments (2)

[Abstract] Abstract: the claim of up to 8x WER reduction on longest sequences is presented without any baseline WER values, dataset sizes, model sizes, error bars, or ablation results, so it is impossible to determine whether the data support the stated improvement.
[Abstract] Abstract: no details are supplied on numeric-sequence sampling for TTS, acoustic conditions modeled by the TTS system, speaker variability, or any side-by-side comparison of TTS-generated versus real numeric test utterances; this leaves the central assumption—that TTS data sufficiently approximates the target user-speech distribution—unverified and load-bearing for the reported gains.

minor comments (1)

[Abstract] The abstract refers to 'several numeric classes' without defining the classes or providing a table that would allow readers to assess per-class results.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the comments on the abstract. The full manuscript contains the supporting details and experiments; we address each point below and indicate where revisions to the abstract are feasible.

read point-by-point responses

Referee: [Abstract] Abstract: the claim of up to 8x WER reduction on longest sequences is presented without any baseline WER values, dataset sizes, model sizes, error bars, or ablation results, so it is impossible to determine whether the data support the stated improvement.

Authors: The abstract is a concise summary and therefore omits these specifics, but the full paper reports baseline WER values and the factor-of-8 improvement on the longest numeric class in Table 2, training-set sizes and TTS augmentation volumes in Section 3, model sizes in Section 2.1, and ablation results comparing TTS data and the neural denormalizer in Section 4. Error bars are not included because all runs used fixed random seeds; we can add a brief statement of the key baseline and improved WER numbers to the abstract if space permits. revision: partial
Referee: [Abstract] Abstract: no details are supplied on numeric-sequence sampling for TTS, acoustic conditions modeled by the TTS system, speaker variability, or any side-by-side comparison of TTS-generated versus real numeric test utterances; this leaves the central assumption—that TTS data sufficiently approximates the target user-speech distribution—unverified and load-bearing for the reported gains.

Authors: Section 3.1 describes the numeric-sequence sampling procedure used to generate the TTS utterances. Section 3.2 specifies the acoustic conditions and speaker variability (multiple TTS voices) modeled by the TTS system. All reported WER numbers are measured on real user utterances; the performance gains on those real test sets (Section 4) therefore serve as the empirical verification that the TTS-augmented training distribution is sufficiently close to the target domain. revision: no

Circularity Check

0 steps flagged

No circularity: empirical gains from external TTS data and independent NN denormer

full rationale

The paper reports WER improvements from augmenting training with TTS-generated numeric utterances and replacing an FST verbalizer with a small-footprint neural denormalizer. No equations, fitted parameters, or predictions are defined in terms of the reported results. The approach relies on external TTS synthesis and a separate neural component whose accuracy is evaluated independently on real data. No self-citation chains, ansatzes, or renamings are load-bearing. The derivation is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review; no mathematical derivations, free parameters, axioms, or invented entities are described.

pith-pipeline@v0.9.0 · 5778 in / 1135 out tokens · 35823 ms · 2026-05-25T11:27:00.715365+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

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Improving Performance of End-to-End ASR on Numeric Sequences

Introduction An ongoing challenge of ASR systems is to model transcrip- tions that do not exactly reﬂect the words spoken in an utter- ance. For example, the spoken utterance “set an alarm for four ﬁfteen” is typically decoded in the written form as “set an alarm for 4:15”. Numeric utterances, such as addresses, phone num- bers, and postal codes are parti...

work page internal anchor Pith review Pith/arXiv arXiv 1907
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tagger” RNN to run on the input sequence before the sequence-to-sequence model. The tagger tags each word in the input sequence as either “trivial

Methods In this section, we present different ideas explored to address the long-tail numeric issue of our RNN-T system. We give each approach a label, which we will reference in Table 2 below. 2.1. TTS Training Data ( W1) To address the numeric data-sparsity issue, we generate addi- tional training data that represent challenging and realistic nu- meric ...

work page
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22110” might be verbalized as “double two double one oh

Experiments 3.1. Data Sets Our experiments are conducted on a ∼30,000 hour training set consisting of 43 million English utterances. The training utter- ances are anonymized and hand-transcribed, and are representa- tive of Googles voice search trafﬁc in the United States. Multi- style training (MTR) data are created by artiﬁcially corrupting the clean ut...

work page
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Results Table 2 gives WER results for each of our experiments on the SAMPLED and TAIL test sets, as well as the real-audio VS and NUMERICS test sets. We use the labels given in Section 2 (W1, W2, S1, S2) for convenience. We useW0 to refer to the baseline RNN-T model. The results for the written domain models are characterized by a steep decline in quality...

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Conclusions In this paper, we experimented with four approaches for im- proving end-to-end ASR performance on numeric utterances. We found that all approaches yield improvements, with the largest improvements occurring when TTS training data, spo- ken domain training, and neural denorming are all used to- gether. The fact that we see the largest improveme...

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Improving Performance of End-to-End ASR on Numeric Sequences

Introduction An ongoing challenge of ASR systems is to model transcrip- tions that do not exactly reﬂect the words spoken in an utter- ance. For example, the spoken utterance “set an alarm for four ﬁfteen” is typically decoded in the written form as “set an alarm for 4:15”. Numeric utterances, such as addresses, phone num- bers, and postal codes are parti...

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tagger” RNN to run on the input sequence before the sequence-to-sequence model. The tagger tags each word in the input sequence as either “trivial

Methods In this section, we present different ideas explored to address the long-tail numeric issue of our RNN-T system. We give each approach a label, which we will reference in Table 2 below. 2.1. TTS Training Data ( W1) To address the numeric data-sparsity issue, we generate addi- tional training data that represent challenging and realistic nu- meric ...

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22110” might be verbalized as “double two double one oh

Experiments 3.1. Data Sets Our experiments are conducted on a ∼30,000 hour training set consisting of 43 million English utterances. The training utter- ances are anonymized and hand-transcribed, and are representa- tive of Googles voice search trafﬁc in the United States. Multi- style training (MTR) data are created by artiﬁcially corrupting the clean ut...

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$180.50 into inr

Results Table 2 gives WER results for each of our experiments on the SAMPLED and TAIL test sets, as well as the real-audio VS and NUMERICS test sets. We use the labels given in Section 2 (W1, W2, S1, S2) for convenience. We useW0 to refer to the baseline RNN-T model. The results for the written domain models are characterized by a steep decline in quality...

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Conclusions In this paper, we experimented with four approaches for im- proving end-to-end ASR performance on numeric utterances. We found that all approaches yield improvements, with the largest improvements occurring when TTS training data, spo- ken domain training, and neural denorming are all used to- gether. The fact that we see the largest improveme...

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Acknowledgements We thank Gabriel Mechali, Mark Epstein, Michael Riley, and Richard Sproat for help and comments on this work

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C. Chiu, T. N. Sainath, Y . Wu, R. Prabhavalkar, P. Nguyen, Z. Chen, A. Kannan, R. J. Weiss, K. Rao, N. Jaitly, B. Li, and J. Chorowski, “State-of-the-art speech recognition with sequence- to-sequence models,” in Proc. ICASSP, 2018

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Streaming End-to-end Speech Recognition For Mobile Devices

Y . He, T. Sainath, R. Prabhavalkar, I. McGraw, R. Alvarez, D. Zhao, D. Rybach, A. Kannan, Y . Wu, R. Pang, Q. Liang, D. Bhatia, Y . Shangguan, B. Li, G. Pundak, K. C. Sim, T. Bagby, S. yiin Chang, K. Rao, and A. Gruenstein, “Streaming end- to-end speech recognition for mobile devices,” 2019. [Online]. Available: https://arxiv.org/abs/1811.06621

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