Waldemar Suszyński

Automatic Detection of Disorders in a Continuous Speech with the Hidden Markov Models Approach

Hidden Markov Models are widely used for recognition of any patterns appearing in an input signal. In the work HMM’s were used to recognize two kind of speech disorders in an acoustic signal: prolongation of fricative phonemes and blockades with repetition of stop phonemes.

Prolongation detection with application of fuzzy logic

Due to great value of the time constant of the integrator circuit, a hardware defined PWM (Pulse Width Modulation) signal makes it possible to build a Digital to Analog Converter (DAC) characterized by a relatively long response time. An attempt at creating the software defined PWM signal leads to increased response time of the DAC converter with a coefficient several hundred times longer than its hardware equivalent. Reorganization of the PWM signal allows for its software synthesis, in that the response time of the DAC converter is only several times larger than its classical equivalent.In the paper we present short profile of different compositions of many-layered applications. We compare speed and stability of the different sets of applications. We describe a configuration which can be used to create a server of scientific information. The application consists of MySQL database management system, http server (Apache) and PHP language. All operate in the Linux environment.

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.

Versatile Remote Access Environment for Computer Networking Laboratory

In this paper the integrated remote access system for a computer networking laboratory is presented. It can be used for scientific, training or engineering purposes. Many features of the proposed solutions are superior to commercially available products.

Computer Visual-Auditory Diagnosis of Speech Non-fluency

The paper focuses on the visual-auditory method of analysis for utterances of stuttering people. The method can be classified as an intermediate solution which is in between a traditional auditory and automatic methods. The author prepared a special computer program DiagLog, with the aim of carrying out the visual-auditory analysis, which can be used by logopaedists to make a diagnosis. The speech disfluencies are assessed by means of the observation of the spectrum and the envelope of fragments of recordings with simultaneous listening to them. A collection of 120 a few-minute recordings of 15 stuttering people was used to verify the correctness of the method and to compare it with the traditional auditory technique. All the samples were analysed by means of the auditory and the visual-auditory method by two independent experts. Consequently, the diagnosis using an additional visual aspect proved itself to be more effective in detecting speech non-fluencies, in classifying and measuring them.

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.