Simulating dysarthric speech for training data augmentation in clinical speech applications

Training machine learning algorithms for speech applications requires large, labeled training data sets. This is problematic for clinical applications where obtaining such data is prohibitively expensive because of privacy concerns or lack of access. As a result, clinical speech applications are typically developed using small data sets with only tens of speakers. In this paper, we propose a method for simulating training data for clinical applications by transforming healthy speech to dysarthric speech using adversarial training. We evaluate the efficacy of our approach using both objective and subjective criteria. We present the transformed samples to five experienced speech-language pathologists (SLPs) and ask them to identify the samples as healthy or dysarthric. The results reveal that the SLPs identify the transformed speech as dysarthric 65% of the time. In a pilot classification experiment, we show that by using the simulated speech samples to balance an existing dataset, the classification accuracy improves by about 10% after data augmentation.Comment: Will appear in Proc. of ICASSP 201

Adaptive threshold optimisation for colour-based lip segmentation in automatic lip-reading systems

A thesis submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, in ful lment of the requirements for the degree of Doctor of Philosophy. Johannesburg, September 2016Having survived the ordeal of a laryngectomy, the patient must come to terms with the resulting loss of speech. With recent advances in portable computing power, automatic lip-reading (ALR) may become a viable approach to voice restoration. This thesis addresses the image processing aspect of ALR, and focuses three contributions to colour-based lip segmentation. The rst contribution concerns the colour transform to enhance the contrast between the lips and skin. This thesis presents the most comprehensive study to date by measuring the overlap between lip and skin histograms for 33 di erent colour transforms. The hue component of HSV obtains the lowest overlap of 6:15%, and results show that selecting the correct transform can increase the segmentation accuracy by up to three times. The second contribution is the development of a new lip segmentation algorithm that utilises the best colour transforms from the comparative study. The algorithm is tested on 895 images and achieves percentage overlap (OL) of 92:23% and segmentation error (SE) of 7:39 %. The third contribution focuses on the impact of the histogram threshold on the segmentation accuracy, and introduces a novel technique called Adaptive Threshold Optimisation (ATO) to select a better threshold value. The rst stage of ATO incorporates -SVR to train the lip shape model. ATO then uses feedback of shape information to validate and optimise the threshold. After applying ATO, the SE decreases from 7:65% to 6:50%, corresponding to an absolute improvement of 1:15 pp or relative improvement of 15:1%. While this thesis concerns lip segmentation in particular, ATO is a threshold selection technique that can be used in various segmentation applications.MT201

Formant analysis in dysphonic patients and automatic Arabic digit speech recognition

<p>Abstract</p> <p>Background and objective</p> <p>There has been a growing interest in objective assessment of speech in dysphonic patients for the classification of the type and severity of voice pathologies using automatic speech recognition (ASR). The aim of this work was to study the accuracy of the conventional ASR system (with Mel frequency cepstral coefficients (MFCCs) based front end and hidden Markov model (HMM) based back end) in recognizing the speech characteristics of people with pathological voice.</p> <p>Materials and methods</p> <p>The speech samples of 62 dysphonic patients with six different types of voice disorders and 50 normal subjects were analyzed. The Arabic spoken digits were taken as an input. The distribution of the first four formants of the vowel /a/ was extracted to examine deviation of the formants from normal.</p> <p>Results</p> <p>There was 100% recognition accuracy obtained for Arabic digits spoken by normal speakers. However, there was a significant loss of accuracy in the classifications while spoken by voice disordered subjects. Moreover, no significant improvement in ASR performance was achieved after assessing a subset of the individuals with disordered voices who underwent treatment.</p> <p>Conclusion</p> <p>The results of this study revealed that the current ASR technique is not a reliable tool in recognizing the speech of dysphonic patients.</p

Speech synthesis : Developing a web application implementing speech technology

Speech is a natural media of communication for humans. Text-to-speech (TTS) technology uses a computer to synthesize speech. There are three main techniques of TTS synthesis. These are formant-based, articulatory and concatenative. The application areas of TTS include accessibility, education, entertainment and communication aid in mass transit. A web application was developed to demonstrate the application of speech synthesis technology. Existing speech synthesis engines for the Finnish language were compared and two open source text to speech engines, Festival and Espeak were selected to be used with the web application. The application uses a Linux-based speech server which communicates with client devices with the HTTP-GET protocol. The application development successfully demonstrated the use of speech synthesis in language learning. One of the emerging sectors of speech technologies is the mobile market due to limited input capabilities in mobile devices. Speech technologies are not equally available in all languages. Text in the Oromo language was tested using Finnish speech synthesizers; due to similar rules in orthography of germination of consonants and length of vowels, legible results were gained

English speaking proficiency assessment using speech and electroencephalography signals

In this paper, the English speaking proficiency level of non-native English speakerswas automatically estimated as high, medium, or low performance. For this purpose, the speech of 142 non-native English speakers was recorded and electroencephalography (EEG) signals of 58 of them were recorded while speaking in English. Two systems were proposed for estimating the English proficiency level of the speaker; one used 72 audio features, extracted from speech signals, and the other used 112 features extracted from EEG signals. Multi-class support vector machines (SVM) was used for training and testing both systems using a cross-validation strategy. The speech-based system outperformed the EEG system with 68% accuracy on 60 testing audio recordings, compared with 56% accuracy on 30 testing EEG recordings