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

Autoencoder bottleneck features with multi-task optimisation for improved continuous dysarthric speech recognition

Automatic recognition of dysarthric speech is a very challenging research problem where performances still lag far behind those achieved for typical speech. The main reason is the lack of suitable training data to accommodate for the large mismatch seen between dysarthric and typical speech. Only recently has focus moved from single-word tasks to exploring continuous speech ASR needed for dictation and most voice-enabled interfaces. This paper investigates improvements to dysarthric continuous ASR. In particular, we demonstrate the effectiveness of using unsupervised autoencoder-based bottleneck (AE-BN) feature extractor trained on out-of-domain (OOD) LibriSpeech data. We further explore multi-task optimisation techniques shown to benefit typical speech ASR. We propose a 5-fold cross-training setup on the widely used TORGO dysarthric database. A setup we believe is more suitable for this low-resource data domain. Results show that adding the proposed AE-BN features achieves an average absolute (word error rate) WER improvement of 2.63% compared to the baseline system. A further reduction of 2.33% and 0.65% absolute WER is seen when applying monophone regularisation and joint optimisation techniques, respectively. In general, the ASR system employing monophone regularisation trained on AE-BN features exhibits the best performance

SYNTHESIZING DYSARTHRIC SPEECH USING MULTI-SPEAKER TTS FOR DSYARTHRIC SPEECH RECOGNITION

Dysarthria is a motor speech disorder often characterized by reduced speech intelligibility through slow, uncoordinated control of speech production muscles. Automatic Speech recognition (ASR) systems may help dysarthric talkers communicate more effectively. However, robust dysarthria-specific ASR requires a significant amount of training speech is required, which is not readily available for dysarthric talkers. In this dissertation, we investigate dysarthric speech augmentation and synthesis methods. To better understand differences in prosodic and acoustic characteristics of dysarthric spontaneous speech at varying severity levels, a comparative study between typical and dysarthric speech was conducted. These characteristics are important components for dysarthric speech modeling, synthesis, and augmentation. For augmentation, prosodic transformation and time-feature masking have been proposed. For dysarthric speech synthesis, this dissertation has introduced a modified neural multi-talker TTS by adding a dysarthria severity level coefficient and a pause insertion model to synthesize dysarthric speech for varying severity levels. In addition, we have extended this work by using a label propagation technique to create more meaningful control variables such as a continuous Respiration, Laryngeal and Tongue (RLT) parameter, even for datasets that only provide discrete dysarthria severity level information. This approach increases the controllability of the system, so we are able to generate more dysarthric speech with a broader range. To evaluate their effectiveness for synthesis of training data, dysarthria-specific speech recognition was used. Results show that a DNN-HMM model trained on additional synthetic dysarthric speech achieves WER improvement of 12.2% compared to the baseline, and that the addition of the severity level and pause insertion controls decrease WER by 6.5%, showing the effectiveness of adding these parameters. Overall results on the TORGO database demonstrate that using dysarthric synthetic speech to increase the amount of dysarthric-patterned speech for training has a significant impact on the dysarthric ASR systems

Computer lipreading via hybrid deep neural network hidden Markov models

Constructing a viable lipreading system is a challenge because it is claimed that only 30% of information of speech production is visible on the lips. Nevertheless, in small vocabulary tasks, there have been several reports of high accuracies. However, investigation of larger vocabulary tasks is rare. This work examines constructing a large vocabulary lipreading system using an approach based-on Deep Neural Network Hidden Markov Models (DNN-HMMs). We present the historical development of computer lipreading technology and the state-ofthe-art results in small and large vocabulary tasks. In preliminary experiments, we evaluate the performance of lipreading and audiovisual speech recognition in small vocabulary data sets. We then concentrate on the improvement of lipreading systems in a more substantial vocabulary size with a multi-speaker data set. We tackle the problem of lipreading an unseen speaker. We investigate the effect of employing several stepstopre-processvisualfeatures. Moreover, weexaminethecontributionoflanguage modelling in a lipreading system where we use longer n-grams to recognise visual speech. Our lipreading system is constructed on the 6000-word vocabulary TCDTIMIT audiovisual speech corpus. The results show that visual-only speech recognition can definitely reach about 60% word accuracy on large vocabularies. We actually achieved a mean of 59.42% measured via three-fold cross-validation on the speaker independent setting of the TCD-TIMIT corpus using Deep autoencoder features and DNN-HMM models. This is the best word accuracy of a lipreading system in a large vocabulary task reported on the TCD-TIMIT corpus. In the final part of the thesis, we examine how the DNN-HMM model improves lipreading performance. We also give an insight into lipreading by providing a feature visualisation. Finally, we present an analysis of lipreading results and suggestions for future development