PreferenceASR is a preference-aware ASR test set built from seven corpora that shows model rankings change when user output-style instructions are considered.
RNN Approaches to Text Normalization: A Challenge
5 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
This paper presents a challenge to the community: given a large corpus of written text aligned to its normalized spoken form, train an RNN to learn the correct normalization function. We present a data set of general text where the normalizations were generated using an existing text normalization component of a text-to-speech system. This data set will be released open-source in the near future. We also present our own experiments with this data set with a variety of different RNN architectures. While some of the architectures do in fact produce very good results when measured in terms of overall accuracy, the errors that are produced are problematic, since they would convey completely the wrong message if such a system were deployed in a speech application. On the other hand, we show that a simple FST-based filter can mitigate those errors, and achieve a level of accuracy not achievable by the RNN alone. Though our conclusions are largely negative on this point, we are actually not arguing that the text normalization problem is intractable using an pure RNN approach, merely that it is not going to be something that can be solved merely by having huge amounts of annotated text data and feeding that to a general RNN model. And when we open-source our data, we will be providing a novel data set for sequence-to-sequence modeling in the hopes that the the community can find better solutions. The data used in this work have been released and are available at: https://github.com/rwsproat/text-normalization-data
representative citing papers
Introduces CN-NewsTTS Bench v0.1 with 1000 records and 992 targets for raw-input Chinese news TTS evaluation, reporting strict accuracies from 0.879 to below 0.60 across seven product systems.
Constructs KVoiceBench, KOpenAudioBench, and KMMAU using agent-driven transfer frameworks from English benchmarks and Korean ASR data, then evaluates eight SpeechLMs to show model-specific gaps and complementary weaknesses between SpokenQA and audio understanding.
Small Qwen3-ASR models fine-tuned on LLM-rewritten spoken-to-formal datasets achieve up to 37.4% relative CER reduction versus verbatim baselines while improving formal text metrics.
TTS-generated numeric training data plus a compact neural denormalizer improve E2E ASR word error rates on numeric sequences by up to a factor of 8 for the longest cases.
citing papers explorer
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Preference-ASR: A Preference-Aware Test Set for Benchmarking ASR in the Era of Speech LLMs
PreferenceASR is a preference-aware ASR test set built from seven corpora that shows model rankings change when user output-style instructions are considered.
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CN-NewsTTS Bench: a target-level automatic benchmark for raw-input Chinese news TTS pronunciation
Introduces CN-NewsTTS Bench v0.1 with 1000 records and 992 targets for raw-input Chinese news TTS evaluation, reporting strict accuracies from 0.879 to below 0.60 across seven product systems.
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KVoiceBench, KOpenAudioBench, and KMMAU: Agent-Driven Korean Speech Benchmarks for Evaluating SpeechLMs
Constructs KVoiceBench, KOpenAudioBench, and KMMAU using agent-driven transfer frameworks from English benchmarks and Korean ASR data, then evaluates eight SpeechLMs to show model-specific gaps and complementary weaknesses between SpokenQA and audio understanding.
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FormalASR: End-to-End Spoken Chinese to Formal Text
Small Qwen3-ASR models fine-tuned on LLM-rewritten spoken-to-formal datasets achieve up to 37.4% relative CER reduction versus verbatim baselines while improving formal text metrics.
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Improving Performance of End-to-End ASR on Numeric Sequences
TTS-generated numeric training data plus a compact neural denormalizer improve E2E ASR word error rates on numeric sequences by up to a factor of 8 for the longest cases.