Papers with automatic speech recognition systems
Automated Cross-language Intelligibility Analysis of Parkinson’s Disease Patients Using Speech Recognition Technologies (P19-2)
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| Challenge: | PD is the second most common neurodegenerative disorder after Alzheimers disease . speech impairments are one of the earliest manifestations in PD patients . |
| Approach: | They propose to analyze the speech signals of PD patients and healthy control subjects in three different languages: German, Spanish, and Czech. |
| Outcome: | The proposed model can discriminate between PD patients and HC subjects even when the language used for train and test is different. |
Voice Query Auto Completion (2021.emnlp-main)
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Raphael Tang, Karun Kumar, Kendra Chalkley, Ji Xin, Liming Zhang, Wenyan Li, Gefei Yang, Yajie Mao, Junho Shin, Geoffrey Craig Murray, Jimmy Lin
| Challenge: | Existing methods fail to complete voice queries from incomplete prefixes because they use orthographic prefix and substrings instead of the true phonetic prefix. |
| Approach: | They propose to condition QAC approaches on intermediate transcriptions to complete voice queries. |
| Outcome: | The proposed method obtains an 18% relative improvement over previous methods on a speech-enabled smart television with real-life voice search traffic. |
Automatic Rephrasing of Transcripts-based Action Items (2021.findings-acl)
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| Challenge: | a growing number of online meetings are transcribed using automated speech recognition systems. |
| Approach: | They propose to use annotated transcripts to generate a human-level rephrasing of an action item . they use supervised text generation techniques and pre-trained transformer models to train models . |
| Outcome: | The proposed model generates a human-level rephrasing of an action item in annotated meeting transcripts. |
Zero-shot Disfluency Detection for Indian Languages (2022.coling-1)
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| Challenge: | Disfluency correction models can help alleviate this problem, but the unavailability of labeled data in low-resource languages impairs progress. |
| Approach: | They propose to use a pretrained multilingual model to detect zero-shot disfluency in Indian languages. |
| Outcome: | The proposed model achieves F1 scores of 75 and higher on five disfluency types across four languages. |
Language technology practitioners as language managers: arbitrating data bias and predictive bias in ASR (2022.lrec-1)
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| Challenge: | despite natural language variation, automatic speech recognition systems perform worse on non-standardised and marginalised language varieties. |
| Approach: | They propose a re-framing of language resources as (public) infrastructure for speech communities . authors propose rethinking of algorithms to address the origins and harms of bias . |
| Outcome: | The proposed approach aims to understand the origins and harms of algorithmic bias and how it can be mitigated. |
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. |
MeetingQA: Extractive Question-Answering on Meeting Transcripts (2023.acl-long)
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| Challenge: | Meeting transcripts are a promising domain for natural language tasks . lack of annotated data impedes research on other important tasks in this domain . |
| Approach: | They propose an extractive QA dataset comprising questions asked by meeting participants and corresponding responses. |
| Outcome: | The proposed dataset extracts questions asked by meeting participants and corresponding responses from transcripts. |