Speech disfluency detection with the correlative method

The presented work constitutes a continuation of research on automatic disfluency recognition in utterances by stuttering people. One of the most frequently occurring episodes are syllable repetitions. The repeated fragments have a similar spectral structure, but they differ in their duration times. In order to detect them, correlation of 1/3 octave spectra was applied in connection with the procedures analysing the amplitude-time structure of sound files. The elaborated computer programme allows for recognition of that type of difluency in continuous speech and for exact, graphically illustrated location of the detected episodes in a sound file. It has been verified on the basis of over a hundred 4-second non-fluent utterances. Its functioning has been examined at various border values of the correlation co-efficient and various widths of the time window. Over 70% efficiency of the automatic detection of the episodes has been achieved. The result is comparable to those achieved with the use of the audio monitoring method

Prolongation detection with application of fuzzy logic

The article presents the method elaborated by the authors for automatic prolongation detection in utterances by the people who stutter. Fricative and nasals consonants were focused upon, as they are the most frequently prolonged ones. Fuzziness was applied in the scales of time, frequency and level of distinctive features of a given manner of articulation. The presented method was verified in continuous speech. It characterizes with almost 90% effectiveness of recognition and high precision of duration measurements of non-fluent episodes

Computer speech echo-corrector

The Echo method, elaborated by Adamczyk, which is commonly applied in many logopaedic clinics, requires the use of the delayed auditory feedback. The devices which perform this task may nowadays be replaced with a typical computer equipped with a sound set and delaying programme. In the article the authors present a real-time application, implemented on the basis of standard sound functions supplied by Microsoft in Windows systems

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

Automatic recognition of non-fluent stops

The presented article constitutes a fragment of research which aims at automatic recognition of disfluencies in the speech of stuttering people. In the utterances of such people difficulties frequently appear in words beginning with stop consonants. On the basis of acoustical analyses of the episodes, procedures for their automatic detection have been elaborated. The procedures apply fuzzy logic

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

Automatic detection of speech disorders with the use of Hidden Markov Model

The most frequently used methods of automatic detection and classification of speech disordersare based on experimental determination of specific distinctive features for a given kind ofdisorder, and working out a suitable algorithm that finds such a disorder in the acoustic signal. Forexample, for detection of prolonged phonemes, analysis of the duration of articulation is used, andon the contrary, phoneme repetition can be detected with the spectrum correlation methods.Additionally, in the case of prolonged phonemes, classification based on their kind is required(nasal or whispered phonemes, vowels, consonants, etc). Therefore, for every kind of a disorder, aseparate algorithm needs to be worked out.Another, more flexible approach is the application of the Hidden Markov Models (HMM). Forthe needs of the presented work, the HMM procedures were implemented and some basic tests ofspeech disorder detection were conducted

Computer program for visual diagnosis of the speech disorders

The diagnosis of speech disorders requires not only audio but also visual analysis. The speaker often performs many articulation moves, e.g.: a movement of mouth without emitting sound. Therefore a computer program was developed for simultaneous analysis of several elements in conjunction with the digital recording of a person's speaking face. These elements are: sound, a spectrogram picture and an oscillogram. The program can read audiovisual recordings and allows for multiple playing with a selected pace. What follows from the previous work with the program, it gives much important information about the kind and the essentiality of the speech disorders