Automatic detection and classification of phoneme repetitions using HTK toolkit

The therapy of stuttering people is based on a proper selection of texts and then on a practice of their articulation by reading or narration. The texts are chosen on the basis of kind and intensity of dysfluencies appearing in a speech. Thus there is still a requirement to find effective and objective methods of analysis of dysfluent speech. Hidden Markov models are stochastic models widely used in recognition of any patterns appearing in a signal. In the work a simple monophone system based on the Hidden Markov Model Toolkit was built and tested in the context of detection and classification of phoneme repetitions - a common speech disorder in the Polish language

Automatic detection of stuttering in a speech

In the work authors applied speech recognition techniques to find disfluent events. The recognition system based on the Hidden Markov Model Toolkit was built and tested. The set of context dependent HMM models was trained and used to locate speech disturbances. Authors were not concentrated on specific disfluency type but tried to find any extraneous sounds in a speech signal. Patients read prepared sentences, the system recognized them and then results were compared to manual transcriptions. It allowed the system to be more robust and enabled to find all disfluencies types appearing at word boundaries. Such system can by utilized in many ways, for example like a "preprocessor" that finds strange sounds in a speech to be analyzed or classified by other algorithms later, to evaluate or track therapy process of stuttering people, to evaluate speech fluency by ´normal´ speakers, etc

Changes in electromyographic signals and skin temperature during standardised effort in volleyball players

The state of athletes’ muscles is not constant, but it differs depending on the stage of sports training, which is associated with different degrees of muscle fatigue. There is thus a need to find a non-invasive and simple method to assess muscle fatigue. The aim of the study was to determine the relationship between muscle fatigue due to physical effort and changes in skin temperature, measured using a thermographic camera. Methods: The study involved 12 volleyball players. The participants were to maintain 70% of peak torque in the joint for as long as possible. We measured peak torque and the time of maintaining 70% of its value (tlim) as well as continuously recording skin temperature and electromyographic (EMG) signals in the region of the belly of the rectus femoris. The measurements were taken twice: before and after a series of squats. Results: The study found that tlim decreased when isometric contraction was performed after physical effort. Pre- and post-exercise skin temperature did not differ significantly, however, the increase rates of temperature and the root mean square (RMS) of the EMG signals grew significantly. In most of the players, skin temperature also correlated with the RMS, median frequency (MDF), and mean frequency (MF) of the EMG signals. Conclusions: Measuring the time of maintaining submaximal torque during isometric contraction and the slope coefficient for the increase in temperature recorded using a thermographic camera can be a simple, cost-effective, and non-invasive method of assessing fatigue and efficiency decreases in the muscles in volleyball players

Automatic detection of prolonged fricative phonemes with the Hidden Markov Models approach

The Hidden Markov Model (HMM) is a stochastic approach to recognition of patterns appearing in an input signal. In the work author's implementation of the HMM were used to recognize speech disorders - prolonged fricative phonemes. To achieve the best recognition effectiveness and simultaneously preserve reasonable time required for calculations two problems need to be addressed: the choice of the HMM and the proper preparation of an input data. Tests results for recognition of the considered type of speech disorders are presented for HMM models with different number of states and for different sizes of codebooks

Improved approach to automatic detection of speech disorders based on the hidden Markov models approach

In the work algorithms commonly utilized in continuous speech recognition systems were applied to detection of speech disorders. The used algorithms were briefly described and the final method of speech disorders detection was presented. The article includes the results of the short test performed in order to check the effectiveness and accuracy of the method. The aim of the test was detection and classification of fricative phonemes prolongation one of the most common speech disorders in the Polish language. It is worth emphasizing that this method enables detection of a category of speech disturbance (e.g. fricative, nasal, vowels, etc… prolongation), but also provides the information about a specific phoneme being disturbed

Speech nonfluency detection and classification based on linear prediction coefficients and neural networks

The goal of the paper is to present a speech nonfluency detection method based on linear prediction coefficients obtained by using the covariance method. The application “Dabar” was created for research. It implements three different methods of LP with the ability to send coefficients computed by them into the input of Kohonen networks. Neural networks were used to classify utterances in categories of fluent and nonfluent. The first one was Kohonen network (SOM), used to reduce LP coefficients representation of each window, which were used as input data to SOM input layer, to a vector of winning neurons of SOM output layer. Radial Basis Function (RBF) networks, linear networks and Multi-Layer Perceptrons were used as classifiers. The research was based on 55 fluent samples and 54 samples with blockades on plosives (p, b, d, t, k, g). The examination was finished with the outcome of 76% classifying

A new elliptical model of the vocal tract

In this paper a new model of the vocal tract is proposed. It is based on elliptical cylinders. It uses the vocal tract model based on PARCOR coefficients and midsaggital measurements of the voice tube. PARCOR coefficients were obtained from linear prediction coefficients which had been obtained by Levinson-Durbin method. Midsaggital lengths, understood as the height of a real vocal tract, were taken from X-Ray pictures, and they were averaged from the vocal tracts of a few people, who uttered the same vowels. The paper bases on Polish vowels: a,e,o,u,i,y

Automatic prolongation recognition in disordered speech using CWT and Kohonen network

Automatic disorder recognition in speech can be very helpful for the therapist while monitoring therapy progress of the patients with disordered speech. In this article we focus on prolongations. We analyze the signal using Continuous Wavelet Transform with 18 bark scales, we divide the result into vectors (using windowing) and then we pass such vectors into Kohonen network. Quite large search analysis was performed (5 variables were checked) during which, recognition above 90% was achieved. All the analysis was performed and the results were obtained using the authors' program - "WaveBlaster". It is very important that the recognition ratio above 90% was obtained by a fully automatic algorithm (without a teacher) from the continuous speech. The presented problem is part of our research aimed at creating an automatic prolongation recognition system

Using the discrete wavelet transform in assessing the effectiveness of rehabilitation in patients after ACL reconstruction

Purpose: The purpose of the current study was to assess the effectiveness of rehabilitation in patients after anterior cruciate ligament reconstruction (ACLR) using a wavelet analysis of the torque-time curve patterns of the extensors of the affected knee. The analysis aimed at the quantitative evaluation of irregularities in these torque-time patterns. Methods: The study involved a group of 22 men who had had ACL reconstruction. The torque-time characteristics were recorded 3, 6 and 12 months after the surgery by an isokinetic dynamometer. They were then examined using the orthogonal Daubechies 4 (Db 4) and biorthogonal Bior 3.1 wavelets. Results: A statistical analysis of the results revealed significant differences in values of the high-frequency energy stored in the details of the signal from the dynamometer between the first and last measurements, both for the Db 4 ( p ≤ 0.023) and Bior 3.1 ( p ≤ 0.01) wavelets. These differences were found in 73% of patients whose curve patterns were analysed using the Db 4 wavelet and in 82% of patients in the case of the Bior 3.1 wavelet. Conclusions: The wavelet transform proved to be an effective research tool in the qualitative evaluation of irregularities occurring in the curve patterns of the torque generated by the extensors of the ACL reconstructed knee. The findings of the study suggest that time-frequency analyses of these characteristics can be of practical importance, as they help assess the state of the patient’s knee joint and his progress in rehabilitation after ACLR