Papers with speech recognition systems
Emotion Impacts Speech Recognition Performance (N19-3)
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| Challenge: | Existing studies show that speech recognition systems depend on multiple factors including lexical content, speaker identity and dialect. |
| Approach: | They propose a method that evaluates the impact of emotion on recognition even when manual transcripts are not available. |
| Outcome: | The proposed method allows to evaluate the impact of emotion on recognition even when manual transcripts are not available. |
Prosody in Cascade and Direct Speech-to-Text Translation: a case study on Korean Wh-Phrases (2024.findings-eacl)
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| Challenge: | Existing direct S2TT systems have been unable to disambiguate utterances where prosody plays a crucial role. |
| Approach: | They propose to use contrastive evaluation to quantitatively measure the ability of direct S2TT systems to disambiguate utterances where prosody plays a crucial role. |
| Outcome: | The proposed system improves overall accuracy 12.9% and improves intent scores 15.6%. |
Learning Robust and Multilingual Speech Representations (2020.findings-emnlp)
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| Challenge: | Unsupervised speech representation learning has shown success at finding representations that correlate with phonetic structures and improve downstream speech recognition performance. |
| Approach: | They evaluate unsupervised speech representation learning representations by looking at their robustness to domain shifts and their ability to improve recognition performance in many languages. |
| Outcome: | The proposed representations improve the recognition performance in 25 phonetically diverse languages and are robust to domain shifts. |
QASR: QCRI Aljazeera Speech Resource A Large Scale Annotated Arabic Speech Corpus (2021.acl-long)
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| Challenge: | QASR is the largest transcribed Arabic speech corpus in the broadcast domain. |
| Approach: | They introduce the largest transcribed Arabic speech corpus, QASR, collected from the broadcast domain. |
| Outcome: | The proposed dataset contains 2,000 hours of speech sampled at 16kHz crawled from Aljazeera news channel. |
Becoming a High-Resource Language in Speech: The Catalan Case in the Common Voice Corpus (2024.lrec-main)
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| Challenge: | a project to create a publicly available voice dataset for speech recognition systems in Catalan is a multifaceted challenge. |
| Approach: | They propose to create a publicly available voice dataset for future speech technologies in Catalan using the Mozilla Common Voice crowd-sourcing platform. |
| Outcome: | The proposed dataset shows that Catalan ranks as the most prominent language in the corpus. |
Text Normalization Infrastructure that Scales to Hundreds of Language Varieties (L18-1)
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| Challenge: | a multi-language text normalization infrastructure is used to train language models for keyboards and speech recognition systems. |
| Approach: | They describe a multi-language text normalization infrastructure that prepares textual data to train language models used in Google's keyboards and speech recognition systems. |
| Outcome: | The proposed system can normalize training data across hundreds of languages . it can detect errors in training data and detect corruption issues . |
Disfluency Correction using Unsupervised and Semi-supervised Learning (2021.eacl-main)
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Nikhil Saini, Drumil Trivedi, Shreya Khare, Tejas Dhamecha, Preethi Jyothi, Samarth Bharadwaj, Pushpak Bhattacharyya
| Challenge: | Disfluencies in conversational speech can affect performance of downstream NLP tasks. |
| Approach: | They propose a disfluency correction model that converts disfluent to fluent text . they use unsupervised encoder-decoder models to generate semi-supervised models . |
| Outcome: | The proposed model achieves a BLEU score of 79.39 on the Switchboard corpus test set and 85.28 with semi-supervision. |
PolyWER: A Holistic Evaluation Framework for Code-Switched Speech Recognition (2024.findings-emnlp)
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| Challenge: | Existing methods for measuring accuracy, such as Word Error Rate (WER), are too strict to address this challenge. |
| Approach: | They propose a framework for evaluating speech recognition systems to handle language-mixing by appending annotations to a publicly available Arabic-English code-switched dataset. |
| Outcome: | The proposed framework evaluates speech recognition systems against human judgement and a publicly available Arabic-English code-switched dataset. |
PronouncUR: An Urdu Pronunciation Lexicon Generator (L18-1)
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| Challenge: | acoustic modeling, large text data and a pronunciation lexicon are the bottlenecks for speech recognition systems for resource scarce languages. |
| Approach: | They propose a grapheme-to-phoneme conversion tool that generates a pronunciation lexicon from a list of Urdu words. |
| Outcome: | The proposed tool predicts pronunciation of words using a LSTM-based model trained on a handcrafted expert lexicon of around 39,000 words and shows an accuracy of 64% upon internal evaluation. |
Multi-Staged Cross-Lingual Acoustic Model Adaption for Robust Speech Recognition in Real-World Applications - A Case Study on German Oral History Interviews (2020.lrec-1)
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| Challenge: | Current automatic speech recognition systems show remarkable performance when adequate data is used for training. |
| Approach: | They propose to perform a robust acoustic model adaption to a target domain in a cross-lingual manner. |
| Outcome: | The proposed approach reduces word error rate by more than 30% on German oral history interviews compared to a model trained from scratch on the target domain and 6-7% on same-language out-of-domain training data. |
Optimized Tokenization for Transcribed Error Correction (2023.emnlp-main)
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| Challenge: | transcribed-like data is often used to correct recurring errors, but training with synthetic data is difficult. |
| Approach: | They propose to use synthetic transcribed-like data to train error correction models . they show that synthetic data outperforms the common approach of random perturbations . |
| Outcome: | The proposed method outperforms the common method using random perturbations in transcribed data and language-specific adjustments to the vocabulary of a BPE tokenizer. |
Improving Speech Recognition for the Elderly: A New Corpus of Elderly Japanese Speech and Investigation of Acoustic Modeling for Speech Recognition (2020.lrec-1)
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| Challenge: | In an aging society, a highly accurate speech recognition system is needed for use in electronic devices for the elderly but this cannot be achieved using conventional speech recognition systems due to the unique features of the speech of elderly people. |
| Approach: | They construct a new corpus of elderly Japanese speech from existing Japanese speech corpora and train them using existing data. |
| Outcome: | The proposed models achieve word error rates (WER) as low as 13.38%, exceeding the results of the previous study. |
SamróMur MilljóN: An ASR Corpus of One Million Verified Read Prompts in Icelandic (2024.lrec-main)
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| Challenge: | samrómur is a crowdsourcing web application designed to collect speech data for the advancement of language technologies in Icelandic. |
| Approach: | They propose to use a crowdsourcing web application to collect and verify Icelandic speech data for automatic speech recognition (ASR) they introduce a dataset comprising one million audio clips from the application . |
| Outcome: | The proposed system can produce high-quality speech data for Icelandic . the proposed system is based on a crowdsourced web application built on Mozilla's Common Voice . |
SoundBreak: A Systematic Study of Audio-Only Adversarial Attacks on Trimodal Models (2026.acl-long)
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| Challenge: | Recent advances in multimodal large language models have increased their vulnerability to adversarial manipulation. |
| Approach: | They propose to target audio-only adversarial attacks on multimodal audio–video–language models . they show that attacks can be successful at low perceptual distortions . |
| Outcome: | The proposed models achieve up to 96% success rate under realistic conditions . the proposed models are more robust to noise than to noise and distortion than to speech recognition systems . |