Objective intelligibility assessment of pathological speakers

Intelligibility is a primary measure for the assessment of pathological speech. Traditionally, it is measured using a perceptual test, which is by definition subjective in nature. Consequently, there is a great interest in reliable, automatic and therefore objective methods. This paper presents such a method that incorporates an automatic speech recognizer (ASR) for producing features that characterize the pronunciations of a speaker and an intelligibility prediction model (IPM) for converting these features into an intelligibility score. High correlations (about 0.90) between objective and perceptual scores are obtained with a system comprising two different speech recognizers: one with traditional acoustic models relating acoustical observations to triphone states and one using phonological features as an intermediate layer between the acoustical observations and the phonetic states

Acoustic-phonetic decoding for speech intelligibility evaluation in the context of Head and Neck Cancers

International audienceIn addition to health problems, Head and Neck Cancers (HNC) can cause serious speech disorders that can lead to partial or complete loss of speech intel-ligibility in some patients. The clinician's evaluation of the intelligibility level before or after surgical treatment and / or during the rehabilitation phase is an important part of the clinical assessment. Perceptive assessment is the most widely used method in clinical practice to assess the level of intelligibility of a patient despite the limitations associated with it such as subjectivity and moderate reproducibility. In this paper, we propose to overcome these limitations by associating a specific task of speech production based on pseudo-words with an automatic speech processing system, both oriented towards acoustic-phonetic decoding. Compared to human perception, the automatic system reaches very high correlation rates and promising results when applied to a French speech corpus including 41 healthy speakers and 85 patients suffering from HNC

Voice and speech outcomes of chemoradiation for advanced head and neck cancer: a systematic review

Purpose of this review is to systematically assess the effects on voice and speech of advanced head and neck cancer and its treatment by means of chemoradiotherapy (CRT). The databases Medline, Embase and Cochrane were searched (1991–2009) for terms head and neck cancer, chemoradiation, voice and speech rehabilitation. Twenty articles met the inclusion criteria, whereof 14 reported on voice outcomes and 10 on speech. Within the selected 20 studies, 18 different tools were used for speech or voice evaluation. Most studies assessed their data by means of patient questionnaires. Four studies presented outcome measures in more than one dimension. Most studies summarised the outcomes of posttreatment data that were assessed at various points in time after treatment. Except for four studies, pre-treatment measurements were lacking. This and the fact that most studies combined the outcomes of patients with radiated laryngeal cancers with outcome data of non-laryngeal cancer patients impedes an interpretation in terms of the effects of radiation versus the effects of the disease itself on voice or speech. Overall, the studies indicated that voice and speech degenerated during CRT, improved again 1–2 months after treatment and exceeded pre-treatment levels after 1 year or longer. However, voice and speech measures do not show normal values before or after treatment. Given the large-ranged posttreatment data, missing baseline assessment and the lacking separation of tumour/radiation sites, there is an urgent need for structured standardised multi-dimensional speech and voice assessment protocols in patients with advanced head and neck cancer treated with CRT

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

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

Models and Analysis of Vocal Emissions for Biomedical Applications

The International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA) came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the newborn to the adult and elderly. Over the years the initial issues have grown and spread also in other fields of research such as occupational voice disorders, neurology, rehabilitation, image and video analysis. MAVEBA takes place every two years in Firenze, Italy. This edition celebrates twenty-two years of uninterrupted and successful research in the field of voice analysis