PEAKS – A system for the automatic evaluation of voice and speech disorders

Maier, Andreas; Haderlein, Tino; Eysholdt, Ulrich; Rosanowski, F.; Batliner, Anton; Schuster, Maria; Nöth, Elmar

doi:10.1016/j.specom.2009.01.004

Cited by 129 publications

(78 citation statements)

References 46 publications

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“…In almost all tasks, it was found that the automated scores correlated with the mean human judgments approximately as well as the judges' individual scores. Similar results were found with the system termed PEAKS [34] wherein speech recognition tools based on hidden Markov models (HMMs) were used to assess speech and voice disorders in subjects with conditions such as a removed larynx and cleft lip or palate. Therefore, automatic assessments of both speech and prosodic disorders are able to perform as well as human judges specifically when the system tends to include the requirements mentioned by [33].…”

Section: E E E P R O O F P R I N T V E R S I O Nsupporting

confidence: 62%

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Automatic Intonation Recognition for the Prosodic Assessment of Language-Impaired Children

Ringeval

Demouy

Szaszák

et al. 2011

IEEE Trans. Audio Speech Lang. Process.

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Abstract-This study presents a preliminary investigation into the automatic assessment of language-impaired children's (LIC) prosodic skills in one grammatical aspect: sentence modalities. Three types of language impairments were studied: autism disorder (AD), pervasive developmental disorder-not otherwise specified (PDD-NOS), and specific language impairment (SLI). A control group of typically developing (TD) children that was both age and gender matched with LIC was used for the analysis. All of the children were asked to imitate sentences that provided different types of intonation (e.g., descending and rising contours). An automatic system was then used to assess LIC's prosodic skills by comparing the intonation recognition scores with those obtained by the control group. The results showed that all LIC have difficulties in reproducing intonation contours because they achieved significantly lower recognition scores than TD children on almost all studied intonations (p 0 05). Regarding the "Rising" intonation, only SLI children had high recognition scores similar to TD children, which suggests a more pronounced pragmatic impairment in AD and PDD-NOS children. The automatic approach used in this study to assess LIC's prosodic skills confirms the clinical descriptions of the subjects' communication impairments.

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Section: E E E P R O O F P R I N T V E R S I O Nsupporting

confidence: 62%

“…Some recent studies have proposed automatic systems to assess prosody production [33], speech disorders [34] or even early literacy [35] in children. Multiple challenges will be faced by such systems in characterizing the prosodic variability of LIC.…”

Section: E E E P R O O F P R I N T V E R S I O Nmentioning

confidence: 99%

Automatic Intonation Recognition for the Prosodic Assessment of Language-Impaired Children

Ringeval

Demouy

Szaszák

et al. 2011

IEEE Trans. Audio Speech Lang. Process.

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“…The use of automatic speech processing techniques in order to allow an automatic speech intelligibility measurement has also been applied to articulation disorders diagnosed for patients with oral squamous cell carcinoma (Stelzle et al, 2011), speakers suffering from dysarthria (Vijayalakshmi et al, 2006;Hummel et al, 2011), or children with cleft lip and palate (CLP) (Maier et al, 2009). …”

Section: Speaker Pathologymentioning

confidence: 99%

A Survey on perceived speaker traits: Personality, likability, pathology, and the first challenge

Schuller

Steidl

Batliner

et al. 2015

Computer Speech & Language

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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.

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“…speech for the same sentence to serve as a baseline, usually of good quality), and consequently of some time-frequency mapping between the tested and reference signals. Finally the approach in (Maier et al, 2009) proposed to predict subjective scores based on the word recognition rate (WRR) obtained by an Automatic Speech Recognition system. It was shown that WRR strongly correlates with the intelligibility of the voice.…”

Section: Introductionmentioning

confidence: 99%