Combining phonological and acoustic ASR-free features for pathological speech intelligibility assessment

Intelligibility is widely used to measure the severity of articulatory problems in pathological speech. Recently, a number of automatic intelligibility assessment tools have been developed. Most of them use automatic speech recognizers (ASR) to compare the patient's utterance with the target text. These methods are bound to one language and tend to be less accurate when speakers hesitate or make reading errors. To circumvent these problems, two different ASR-free methods were developed over the last few years, only making use of the acoustic or phonological properties of the utterance. In this paper, we demonstrate that these ASR-free techniques are also able to predict intelligibility in other languages. Moreover, they show to be complementary, resulting in even better intelligibility predictions when both methods are combined

Articulatory and bottleneck features for speaker-independent ASR of dysarthric speech

The rapid population aging has stimulated the development of assistive devices that provide personalized medical support to the needies suffering from various etiologies. One prominent clinical application is a computer-assisted speech training system which enables personalized speech therapy to patients impaired by communicative disorders in the patient's home environment. Such a system relies on the robust automatic speech recognition (ASR) technology to be able to provide accurate articulation feedback. With the long-term aim of developing off-the-shelf ASR systems that can be incorporated in clinical context without prior speaker information, we compare the ASR performance of speaker-independent bottleneck and articulatory features on dysarthric speech used in conjunction with dedicated neural network-based acoustic models that have been shown to be robust against spectrotemporal deviations. We report ASR performance of these systems on two dysarthric speech datasets of different characteristics to quantify the achieved performance gains. Despite the remaining performance gap between the dysarthric and normal speech, significant improvements have been reported on both datasets using speaker-independent ASR architectures.Comment: to appear in Computer Speech & Language - https://doi.org/10.1016/j.csl.2019.05.002 - arXiv admin note: substantial text overlap with arXiv:1807.1094

Targeting the expressive language of children with Down syndrome who are minimally verbal : bridging research and practice

textChildren with Down syndrome present with an array of physical and cognitive sequelae that can hinder speech and language development. These individuals can constitute a considerable portion of a speech-language pathologist’s caseload. Based on the principles of best evidence, clinicians are ethically responsible for providing the most effective treatment for their clients. The available literature focuses mainly on describing the linguistic characteristics in this population, while relatively less focus is placed on effective intervention programs. This paper investigates the available evidence regarding speech and language interventions for children with Down syndrome who are in the mild to moderate range of linguistic functioning, and provides an outlook for future research based on best evidence.Communication Sciences and Disorder

Speech vocoding for laboratory phonology

Using phonological speech vocoding, we propose a platform for exploring relations between phonology and speech processing, and in broader terms, for exploring relations between the abstract and physical structures of a speech signal. Our goal is to make a step towards bridging phonology and speech processing and to contribute to the program of Laboratory Phonology. We show three application examples for laboratory phonology: compositional phonological speech modelling, a comparison of phonological systems and an experimental phonological parametric text-to-speech (TTS) system. The featural representations of the following three phonological systems are considered in this work: (i) Government Phonology (GP), (ii) the Sound Pattern of English (SPE), and (iii) the extended SPE (eSPE). Comparing GP- and eSPE-based vocoded speech, we conclude that the latter achieves slightly better results than the former. However, GP - the most compact phonological speech representation - performs comparably to the systems with a higher number of phonological features. The parametric TTS based on phonological speech representation, and trained from an unlabelled audiobook in an unsupervised manner, achieves intelligibility of 85% of the state-of-the-art parametric speech synthesis. We envision that the presented approach paves the way for researchers in both fields to form meaningful hypotheses that are explicitly testable using the concepts developed and exemplified in this paper. On the one hand, laboratory phonologists might test the applied concepts of their theoretical models, and on the other hand, the speech processing community may utilize the concepts developed for the theoretical phonological models for improvements of the current state-of-the-art applications

Large scale evaluation of importance maps in automatic speech recognition

In this paper, we propose a metric that we call the structured saliency benchmark (SSBM) to evaluate importance maps computed for automatic speech recognizers on individual utterances. These maps indicate time-frequency points of the utterance that are most important for correct recognition of a target word. Our evaluation technique is not only suitable for standard classification tasks, but is also appropriate for structured prediction tasks like sequence-to-sequence models. Additionally, we use this approach to perform a large scale comparison of the importance maps created by our previously introduced technique using "bubble noise" to identify important points through correlation with a baseline approach based on smoothed speech energy and forced alignment. Our results show that the bubble analysis approach is better at identifying important speech regions than this baseline on 100 sentences from the AMI corpus.Comment: submitted to INTERSPEECH 202