Speech is a complex process that can break in many different ways and lead to a variety of voice disorders. Dysarthria is a voice disorder where individuals are unable to control one or more of the aspects of speech-the articulation, breathing, voicing, or prosody-leading to less intelligible speech. In this paper, we evaluate the accuracy of state-of-the-art automatic speech recognition systems (ASRs) on two dysarthric speech datasets and compare the results to ASR performance on control speech. The limits of ASR performance using different voices have not been explored since the field has shifted from generative models of speech recognition to deep neural network architectures. To test how far the field has come in recognizing disordered speech, we test two different ASR systems: (1) Carnegie Mellon University's Sphinx Open Source Recognition, and (2) Google®Speech Recognition. While (1) uses generative models of speech recognition, (2) uses deep neural networks. As expected, while (2) achieved lower word error rates (WER) on dysarthric speech than (1), control speech had a WER 59% lower than dysarthric speech. Future studies should be focused not only on making ASRs robust to environmental noise, but also more robust to different voices.

We present a new metadataset which provides insight into where and how two ASR systems make errors on several different speech datasets. By making this data readily available to researchers, we hope to stimulate research in the area of WER estimation models, in order to gain a deeper understanding of how intelligibility is encoded in speech. Using this dataset, we attempt to estimate intelligibility using a state-of-the-art model for speech quality estimation and found that this model did not work to model speech intelligibility. This finding sheds light on the relationship between how speech quality is encoded in acoustic features and how intelligibility is encoded. It shows that we have a lot more to learn in how to effectively model intelligibility. It is our hope that the metadataset we present will stimulate research into creating systems that more effectively model intelligibility.

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