PAoS Markers: Trajectory Analysis of Selective Phonological Posteriors for Assessment of Progressive Apraxia of Speech

Progressive apraxia of Speech (PAoS) is a progressive motor speech disorder associated with neurodegenerative disease causing impairment of phonetic encoding and motor speech planning. Clinical observation and acoustic studies show that duration analysis provides reliable cues for diagnosis of the disease progression and severity of articulatory disruption. The goal of this paper is to develop computational methods for objective evaluation of duration and trajectory of speech articulation. We use phonological posteriors as speech features. Phonological posteriors consist of probabilities of phonological classes estimated for every short segment of the speech signal. PAoS encompasses lengthening of duration which is more pronounced in vowels; we thus hypothesize that a small subset of phonological classes provide stronger evidence for duration and trajectory analysis. These classes are determined through analysis of linear prediction coefficients (LPC). To enable trajectory analysis without phonetic alignment, we exploit phonological structures defined through quantization of phonological posteriors. Duration and trajectory analysis are conducted on blocks of multiple consecutive segments possessing similar phonological structures. Moreover, unique phonological structures are identified for every severity condition

A survey on perceived speaker traits: personality, likability, pathology, and the first challenge

The INTERSPEECH 2012 Speaker Trait Challenge aimed at a unified test-bed for perceived speaker traits – the first challenge of this kind: personality in the five OCEAN personality dimensions, likability of speakers, and intelligibility of pathologic speakers. In the present article, we give a brief overview of the state-of-the-art in these three fields of research and describe the three sub-challenges in terms of the challenge conditions, the baseline results provided by the organisers, and a new openSMILE feature set, which has been used for computing the baselines and which has been provided to the participants. Furthermore, we summarise the approaches and the results presented by the participants to show the various techniques that are currently applied to solve these classification tasks

Towards an ASR-free objective analysis of pathological speech

Nowadays, intelligibility is a popular measure of the severity of the articulatory deficiencies of a pathological speaker. Usually, this measure is obtained by means of a perceptual test, consisting of nonconventional and/or nonconnected words. In previous work, we developed a system incorporating two Automatic Speech Recognizers (ASR) that could fairly accurately estimate phoneme intelligibility (PI). In the present paper, we propose a novel method that aims to assess the running speech intelligibility (RSI) as a more relevant indicator of the communication efficiency of a speaker in a natural setting. The proposed method computes a phonological characterization of the speaker by means of a statistical analysis of frame-level phonological features. Important is that this analysis requires no knowledge of what the speaker was supposed to say. The new characterization is demonstrated to predict PI and to provide valuable information about the nature and severity of the pathology

Automatic analysis of pathological speech

De ernst van een spraakstoornis wordt vaak gemeten a.d.h.v. spraakverstaanbaarheid. Deze maat wordt in de klinische praktijk vaak bepaald met een perceptuele test. Zo’n test is van nature subjectief vermits de therapeut die de test afneemt de (stoornis van de) patiënt vaak kent en ook vertrouwd is met het gebruikte testmateriaal. Daarom is het interessant te onderzoeken of men met spraakherkenning een objectieve beoordelaar van verstaanbaarheid kan creëren. In deze thesis wordt een methodologie uitgewerkt om een gestandaardiseerde perceptuele test, het Nederlandstalig Spraakverstaanbaarheidsonderzoek (NSVO), te automatiseren. Hiervoor wordt gebruik gemaakt van spraakherkenning om de patiënt fonologisch en fonemisch te karakteriseren en uit deze karakterisering een spraakverstaanbaarheidsscore af te leiden. Experimenten hebben aangetoond dat de berekende scores zeer betrouwbaar zijn. Vermits het NSVO met nonsenswoorden werkt, kunnen vooral kinderen hierdoor leesfouten maken. Daarom werden nieuwe methodes ontwikkeld, gebaseerd op betekenisdragende lopende spraak, die hiertegen robuust zijn en tegelijk ook in verschillende talen gebruikt kunnen worden. Met deze nieuwe modellen bleek het mogelijk te zijn om betrouwbare verstaanbaarheidsscores te berekenen voor Vlaamse, Nederlandse en Duitse spraak. Tenslotte heeft het onderzoek ook belangrijke stappen gezet in de richting van een automatische karakterisering van andere aspecten van de spraakstoornis, zoals articulatie en stemgeving