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EasyCall corpus: a dysarthric speech dataset

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arxiv 2104.02542 v1 pith:5QXHE63K submitted 2021-04-06 cs.CL

classification cs.CL
keywords dysarthriccorpusdatasetspeechcommandseasycallapplicationassistive
verification ladder T0 review T1 audit T2 compute T3 formal
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This paper introduces a new dysarthric speech command dataset in Italian, called EasyCall corpus. The dataset consists of 21386 audio recordings from 24 healthy and 31 dysarthric speakers, whose individual degree of speech impairment was assessed by neurologists through the Therapy Outcome Measure. The corpus aims at providing a resource for the development of ASR-based assistive technologies for patients with dysarthria. In particular, it may be exploited to develop a voice-controlled contact application for commercial smartphones, aiming at improving dysarthric patients' ability to communicate with their family and caregivers. Before recording the dataset, participants were administered a survey to evaluate which commands are more likely to be employed by dysarthric individuals in a voice-controlled contact application. In addition, the dataset includes a list of non-commands (i.e., words near/inside commands or phonetically close to commands) that can be leveraged to build a more robust command recognition system. At present commercial ASR systems perform poorly on the EasyCall Corpus as we report in this paper. This result corroborates the need for dysarthric speech corpora for developing effective assistive technologies. To the best of our knowledge, this database represents the richest corpus of dysarthric speech to date.

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Cited by 2 Pith papers

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  1. A Self-Training Approach for Whisper to Enhance Long Dysarthric Speech Recognition

    cs.SD 2025-06 conditional novelty 6.0 of 10

    An iterative segmentation-based self-training method for Whisper improved long dysarthric speech recognition and achieved second place in both WER and SemScore at the SAP Challenge.

  2. Voice Quality Dimensions as Interpretable Primitives for Speaking Style for Atypical Speech and Affect

    cs.SD 2025-05 conditional novelty 5.0 of 10

    Linear probes on frozen speech embeddings predict seven voice quality ratings and transfer zero-shot across languages, tasks, and affect.

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