Abstract 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.
Shahamiri and Salim @cite_42 proposed an artificial neural network-based system trained on digit utterances from nine non-dysarthric and 13 dysarthric individuals affected by Cerebral Palsy (CP). @cite_23 trained their models solely on 18 hours of speech of 15 dysarthric speakers due to CP leaving one speaker out as test set. Rudzicz @cite_40 compared the performance of a speaker-dependent and a speaker-adaptive GMM-HMM systems on the Nemours database @cite_22 . Later, Rudzicz @cite_30 tried using AF together with conventional acoustic features for phone classification experiments on dysarhtric speech. Mengistu and Rudzicz @cite_38 combined dysarthric data of eight dysarthric speakers with that of seven normal speakers, leaving one out as test set and obtained an average increase by 13.0 In one of the earliest work on Dutch pathological speech by @cite_19 , a pilot study was presented on ASR of Dutch dysarthric speech data obtained from two speakers with a birth defect and a cerebrovascular accident. Both speakers were classified as mild dysarthric by a speech pathologist. @cite_60 proposed a weighted finite state transducer (WSFT)-based ASR correction technique applied to an ASR system trained. Similar work had been proposed by Caballero-Morales and Cox @cite_28 previously.
@cite_30 @cite_38 @cite_22 @cite_60 @cite_28 @cite_42 @cite_19 @cite_40 @cite_23
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Dysarthria is a neurological impairment of controlling the motor speech articulators that compromises the speech signal. Automatic Speech Recognition (ASR) can be very helpful for speakers with dysarthria because the disabled persons are often physically incapacitated. Mel-Frequency Cepstral Coefficients (MFCCs) have been proven to be an appropriate representation of dysarthric speech, but the question of which MFCC-based feature set represents dysarthric acoustic features most effectively has not been answered. Moreover, most of the current dysarthric speech recognisers are either speaker-dependent (SD) or speaker-adaptive (SA), and they perform poorly in terms of generalisability as a speaker-independent (SI) model. First, by comparing the results of 28 dysarthric SD speech recognisers, this study identifies the best-performing set of MFCC parameters, which can represent dysarthric acoustic features to be used in Artificial Neural Network (ANN)-based ASR. Next, this paper studies the application of ANNs as a fixed-length isolated-word SI ASR for individuals who suffer from dysarthria. The results show that the speech recognisers trained by the conventional 12 coefficients MFCC features without the use of delta and acceleration features provided the best accuracy, and the proposed SI ASR recognised the speech of the unforeseen dysarthric evaluation subjects with word recognition rate of 68.38 . Speech-driven assistive technology can be an attractive alternative to conventional interfaces for people with physical disabilities. However, often the lack of motor-control of the speech articulators results in disordered speech, as condition known as dysarthria. Dysarthric speakers can generally not obtain satisfactory performances with off-the-shelf automatic speech recognition (ASR) products and disordered speech ASR is an increasingly active research area. Sparseness of suitable data is a big challenge. The experiments described here use UAspeech, one of the largest dysarthric databases available, which is still easily an order of magnitude smaller than typical speech databases. This study investigates how far fundamental training and adaptation techniques developed in the LVCSR community can take us. A variety of ASR systems using maximum likelihood and MAP adaptation strategies are established with all speakers obtaining significant improvements compared to the baseline system regardless of the severity of their condition. The best systems show on average 34 relative improvement on known published results. An analysis of the correlation between intelligibility of the speaker and the type of system which would represent an optimal operating point in terms of performance shows that for severely dysarthric speakers, the exact choice of system configuration is more critical than for speakers with less disordered speech. The effectiveness of introducing deep neural networks into conventional speaker recognition pipelines has been broadly shown to benefit system performance. A novel text-independent speaker verification (SV) framework based on the triplet loss and a very deep convolutional neural network architecture (i.e., Inception-Resnet-v1) are investigated in this study, where a fixed-length speaker discriminative embedding is learned from sparse speech features and utilized as a feature representation for the SV tasks. A concise description of the neural network based speaker discriminative training with triplet loss is presented. An Euclidean distance similarity metric is applied in both network training and SV testing, which ensures the SV system to follow an end-to-end fashion. By replacing the final max average pooling layer with a spatial pyramid pooling layer in the Inception-Resnet-v1 architecture, the fixed-length input constraint is relaxed and an obvious performance gain is achieved compared with the fixed-length input speaker embedding system. For datasets with more severe training test condition mismatches, the probabilistic linear discriminant analysis (PLDA) back end is further introduced to replace the distance based scoring for the proposed speaker embedding system. Thus, we reconstruct the SV task with a neural network based front-end speaker embedding system and a PLDA that provides channel and noise variabilities compensation in the back end. Extensive experiments are conducted to provide useful hints that lead to a better testing performance. Comparison with the state-of-the-art SV frameworks on three public datasets (i.e., a prompt speech corpus, a conversational speech Switchboard corpus, and NIST SRE10 10 s–10 s condition) justifies the effectiveness of our proposed speaker embedding system. We propose a novel adversarial multi-task learning scheme, aiming at actively curtailing the inter-talker feature variability while maximizing its senone discriminability so as to enhance the performance of a deep neural network (DNN) based ASR system. We call the scheme speaker-invariant training (SIT). In SIT, a DNN acoustic model and a speaker classifier network are jointly optimized to minimize the senone (tied triphone state) classification loss, and simultaneously mini-maximize the speaker classification loss. A speaker-invariant and senone-discriminative deep feature is learned through this adversarial multi-task learning. With SIT, a canonical DNN acoustic model with significantly reduced variance in its output probabilities is learned with no explicit speaker-independent (SI) transformations or speaker-specific representations used in training or testing. Evaluated on the CHiME-3 dataset, the SIT achieves 4.99 relative word error rate (WER) improvement over the conventional SI acoustic model. With additional unsupervised speaker adaptation, the speaker-adapted (SA) SIT model achieves 4.86 relative WER gain over the SA SI acoustic model.