Papers with ASR model
STT4SG-350: A Speech Corpus for All Swiss German Dialect Regions (2023.acl-short)
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Michel Plüss, Jan Deriu, Yanick Schraner, Claudio Paonessa, Julia Hartmann, Larissa Schmidt, Christian Scheller, Manuela Hürlimann, Tanja Samardžić, Manfred Vogel, Mark Cieliebak
| Challenge: | We present a corpus of Swiss German speech annotated with Standard German text at the sentence level. |
| Approach: | They present a corpus of Swiss German speech annotated with Standard German sentences . they use a web app to show the speakers standard German sentences and record them . |
| Outcome: | The corpus contains 343 hours of speech from all Swiss German dialect regions . it is the largest public speech corpus for Swiss German to date . |
RED-ACE: Robust Error Detection for ASR using Confidence Embeddings (2022.emnlp-main)
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| Challenge: | ASR Error Detection (AED) models post-process the output of Automatic Speech Recognition systems, in order to detect transcription errors. |
| Approach: | They propose to use ASR model's word-level confidence scores to combine ASR models with transcribed text to improve AED performance. |
| Outcome: | The proposed models combine the confidence scores and transcribed text into a contextualized representation. |
End-to-End Speech Recognition and Disfluency Removal (2020.findings-emnlp)
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| Challenge: | Disfluency detection is usually an intermediate step between an automatic speech recognition system and a downstream task. |
| Approach: | They propose to train models to directly map disfluent speech into fluent transcripts without relying on a separate disfluency detection model. |
| Outcome: | The proposed models learn to generate fluent transcripts, but their performance is slightly worse than a baseline pipeline approach consisting of an ASR system and a specialized disfluency detection model. |
DITTO: Data-efficient and Fair Targeted Subset Selection for ASR Accent Adaptation (2023.acl-long)
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Suraj Kothawade, Anmol Mekala, D.Chandra Sekhara Hetha Havya, Mayank Kothyari, Rishabh Iyer, Ganesh Ramakrishnan, Preethi Jyothi
| Challenge: | State-of-the-art automatic speech recognition systems exhibit disparate performance on varying speech accents. |
| Approach: | They propose to use submodular mutual information to find the most informative set of utterances matching a target accent within a fixed budget. |
| Outcome: | The proposed model is 3-5 times more label-efficient on the Indic-TTS and L2 datasets than other methods. |
Error-preserving Automatic Speech Recognition of Young English Learners’ Language (2024.acl-long)
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| Challenge: | State-of-the-art speech recognition models are often trained on adult read-aloud data by native speakers and do not transfer well to young language learners’ speech. |
| Approach: | They propose to use an automated speech recognition module to train language learners' speaking skills on spontaneous speech by young language learners. |
| Outcome: | The proposed model improves on 85 hours of English audio spoken by Swiss learners and preserves their mistakes. |
Beyond Common Words: Enhancing ASR Cross-Lingual Proper Noun Recognition Using Large Language Models (2024.findings-emnlp)
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| Challenge: | In this work, we address the challenge of cross-lingual proper noun recognition in automatic speech recognition systems where proper nodes in an utterance may originate from a language different from the language in which the ASR system is trained. |
| Approach: | They propose a dictionary-based method to correct ASR predictions in a large language model . |
| Outcome: | The proposed method significantly reduces word error rates across cross-lingual proper noun recognition tasks involving three secondary languages. |
MuPe Life Stories Dataset: Spontaneous Speech in Brazilian Portuguese with a Case Study Evaluation on ASR Bias against Speakers Groups and Topic Modeling (2025.coling-main)
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Sidney Evaldo Leal, Arnaldo Candido Junior, Ricardo Marcacini, Edresson Casanova, Odilon Gonçalves, Anderson Silva Soares, Rodrigo Freitas Lima, Lucas Rafael Stefanel Gris, Sandra Aluísio
| Challenge: | Recent datasets for automatic speech recognition in Brazilian Portuguese lack diversity in terms of age groups, regional accents, and education levels. |
| Approach: | They propose to use a dataset to analyze the impact of ASR in Brazilian Portuguese (BP) they demonstrate that current models are biased regarding age, education, and regional accents. |
| Outcome: | The proposed dataset helps mitigate biases in current ASR models regarding education levels and age groups. |
Sequential Randomized Smoothing for Adversarially Robust Speech Recognition (2021.emnlp-main)
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| Challenge: | Existing, naive defenses against adversarial attacks are lagging . a new paper aims to break these defenses with adaptive noise ensembling . |
| Approach: | They propose a randomized smoothing paradigm that can be used to break adversarial attacks . they use speech enhancement methods and a novel use for ASR output ensembling methods . |
| Outcome: | The proposed model is robust to all attacks that use inaudible noise and can only be broken with very high distortion. |
Automatic Speech Recognition Datasets in Cantonese: A Survey and New Dataset (2022.lrec-1)
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Tiezheng Yu, Rita Frieske, Peng Xu, Samuel Cahyawijaya, Cheuk Tung Yiu, Holy Lovenia, Wenliang Dai, Elham J. Barezi, Qifeng Chen, Xiaojuan Ma, Bertram Shi, Pascale Fung
| Challenge: | In this paper, we address the problem of data scarcity for the Hong Kong Cantonese language . due to the popularization of deep learning, ASR technology has led to a significant improvement in recognizing many languages. |
| Approach: | They propose to use a dataset to analyze the data available for the Hong Kong Cantonese language . they use zh-HK as a source and a state-of-the-art ASR model to build a powerful model . |
| Outcome: | The proposed model improves on the biggest existing dataset, Common Voice zh-HK. |
Killkan: The Automatic Speech Recognition Dataset for Kichwa with Morphosyntactic Information (2024.lrec-main)
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| Challenge: | Existing datasets for automatic speech recognition (ASR) in the endangered Kichwa language have been limited. |
| Approach: | They present Killkan, the first dataset for automatic speech recognition (ASR) in the Kichwa language, an indigenous language of Ecuador. |
| Outcome: | The proposed dataset shows that it can be used to build an automatic speech recognition system for the endangered language with reliable quality despite its small size. |