Ireneusz Codello

The Prolongation-Type Speech Non-fluency Detection Based on the Linear Prediction Coefficients and the Neural Networks

The goal of the paper is presenting a speech prolongation detection method based on the linear predicion coefficients obtained by the Levinson-Durbin method. The application “Dabar”, which was made for this aim, has an ability of setting the coefficients computed by the implemented methods as an input of the Kohonen networks with different size of the output layer. Three different types of the neural networks were used to classify fluency of the utterances: RBF networks, linear networks and Multi-Layer Perceptrons. The Kohonen network (SOM) was used to reduce the LP coefficients representation to the winning neurons vector. After that the vector was splitted into subvectors whom represents 400ms utterances. These utterances were fragments of the Polish speech without the silence. The research was based on 202 fluent utterances and 140 with the prolongations on Polish phonems. The classifying success reached 75% of certainty.

Automatic Disordered Syllables Repetition Recognition in Continuous Speech Using CWT and Correlation

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 the syllables repetition are described. The signal was analyzed using Continuous Wavelet Transform with bark scales, the result was divided into vectors (using windowing) and then a correlation algorithm was used on this data. Quite large search analysis was performed during which, recognition above 80% 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 80% 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 disorders recognition system.

Time–frequency Analysis of the EMG Digital Signals

In the article comparison of time-frequency spectra of EMG signals obtained by the following methods: Fast Fourier Transform, predictive analysis and wavelet analysis is presented. The EMG spectra of biceps and triceps while an adult man was flexing his arm were analysed. The advantages of the predictive analysis were shown as far as averaging of the spectra and determining the main maxima are concerned. The Continuous Wavelet Transform method was applied, which allows for the proper distribution of the scales, aiming at an accurate analysis and localisation of frequency maxima as well as the identification of impulses which are characteristic of such signals (bursts) in the scale of time. The modified Morlet wavelet was suggested as the mother wavelet. The wavelet analysis allows for the examination of the changes in the frequency spectrum in particular stages of the muscle contraction. Predictive analysis may also be very useful while smoothing and averaging the EMG signal spectrum in time.

Automatic Syllable Repetition Detection in Continuous Speech Based on Linear Prediction Coefficients

The goal of this paper is to present a syllable repetition detection method based on linear prediction coefficients obtained by the Levinson–Durbin method. The algorithm wrought by the authors of this paper is based on the linear prediction spectrum. At first the utterance is automatically split into continuous fragments that correspond with syllables. Next, for each of them the formant maps are being obtained. After dimension reduction by the K-means method they are being compared. The algorithm was verified based on 56 continuous utterances of 14 stutterers. They contain fluent parts, as well as syllable repetitions on Polish phonemes. The classifying success reached 90 % of sensitivity with 75–80% precision.

Utterance intonation imaging using the cepstral analysis

Speech intonation consists mainly of fundamental frequency, i.e. the frequency of vocal cord vibrations. Finding those frequency changes can be very useful-for instance, studying foreign languages where speech intonation is an inseparable part of a language (like grammar or vocabulary). In our work we present the cepstral algorithm for F0 finding as well as an application for facilitating utterance intonation learning.