  Automatic classification of disordered speech can provide an objective tool
for identifying the presence and severity of speech impairment. Classification
approaches can also help identify hard-to-recognize speech samples to teach ASR
systems about the variable manifestations of impaired speech. Here, we develop
and compare different deep learning techniques to classify the intelligibility
of disordered speech on selected phrases. We collected samples from a diverse
set of 661 speakers with a variety of self-reported disorders speaking 29 words
or phrases, which were rated by speech-language pathologists for their overall
intelligibility using a five-point Likert scale. We then evaluated classifiers
developed using 3 approaches: (1) a convolutional neural network (CNN) trained
for the task, (2) classifiers trained on non-semantic speech representations
from CNNs that used an unsupervised objective [1], and (3) classifiers trained
on the acoustic (encoder) embeddings from an ASR system trained on typical
speech [2]. We found that the ASR encoder's embeddings considerably outperform
the other two on detecting and classifying disordered speech. Further analysis
shows that the ASR embeddings cluster speech by the spoken phrase, while the
non-semantic embeddings cluster speech by speaker. Also, longer phrases are
more indicative of intelligibility deficits than single words.
