Browsing by Subject "Speech analysis"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Publication
    Open Access
    Análisis del discurso de los estudiantes de Magisterio sobre la contribución del practicum al desarrollo de su identidad profesional docente
    (Universidad de Murcia. Servicio de Publicaciones, 2018) Pérez Ferra, Miguel; Quijano López, Rocío
    El estudio responde al análisis del discurso de los estudiantes de Magisterio sobre la incidencia del practicum en la construcción de su identidad profesional docente. Se partió de setenta y seis estudiantes, que han cursado el practicum de cuarto curso en 2016-17, Educación Primaria, pertenecientes a la Universidad de Jaén, seleccionando veintiuna narrativas, en función de la calidad de los relatos. Se han utilizado dos metodologías complementarias: el Análisis crítico del discurso y la Teoría fundamentada. Las narrativas de los estudiantes evidencian un discurso en torno a cuatro grandes categorías: “profesionalización docente”, “uso de metodologías”, “sentido ético” y “vocación”, ámbitos que se manifiestan en un discurso diferenciado; bien en torno a un sentido racional de concebir la enseñanza; bien desde una percepción meramente humanística. En la propuesta de cambio se alude a la necesidad que tiene la Universidad de gestionar lo nuevo desde la tradición.
  • Thumbnail Image
    Publication
    Open Access
    Recognition of Spanish consonants in 8-talker babble by children with cochlear implants, and by children and adults with normal hearing
    (Acoustical Society of America, 2018-07-05) Moreno-Torres, Ignacio; Madrid Cánovas, Sonia; Lengua Española y Lingüística General
    This paper presents the results of closed-set recognition task for 80 Spanish consonant-vowel sounds in 8-talker babble. Three groups of subjects participated in the study: a group of children using cochlear implants (CIs; age range: 7–13), an age-matched group of children with normal hearing (NH), and a group of adults with NH. The speech-to-noise ratios at which the participants recognized 33% of the target consonants were +7.8 dB, −3 dB, and −6 dB, respectively. In order to clarify the qualitative differences between the groups, groups were matched for the percentage of recognized syllables. As compared with the two groups with NH, the children with CIs: (1) produced few “I do not know” responses; (2) frequently selected the voiced stops (i.e., /b, d, ɡ/) and the most energetic consonants (i.e., /l, r, ʝ, s, ʧ/); (3) showed no vowel context effects; and (4) had a robust voicing bias. As compared with the adults with NH, both groups of children showed a fronting bias in place of articulation errors. The factors underlying these error patterns are discussed.
  • Thumbnail Image
    Publication
    Open Access
    Ten years of research on automatic voice and speech analysis of people with Alzheimer's disease and mild cognitive impairment : a systematic review article
    (Frontiers Media, 2021-03-23) Martínez-Nicolás, Israel; Llorente, Thide; Martínez-Sánchez, Francisco; García Meilá, Juan José; Psicología Básica y Metodología
    Background: The field of voice and speech analysis has become increasingly popular over the last 10 years, and articles on its use in detecting neurodegenerative diseases have proliferated. Many studies have identified characteristic speech features that can be used to draw an accurate distinction between healthy aging among older people and those with mild cognitive impairment and Alzheimer’s disease. Speech analysis has been singled out as a cost-effective and reliable method for detecting the presence of both conditions. In this research, a systematic review was conducted to determine these features and their diagnostic accuracy. Methods: Peer-reviewed literature was located across multiple databases, involving studies that apply new procedures of automatic speech analysis to collect behavioral evidence of linguistic impairments along with their diagnostic accuracy on Alzheimer’s disease and mild cognitive impairment. The risk of bias was assessed by using JBI and QUADAS-2 checklists. Results: Thirty-five papers met the inclusion criteria; of these, 11 were descriptive studies that either identified voice features or explored their cognitive correlates, and the rest were diagnostic studies. Overall, the studies were of good quality and presented solid evidence of the usefulness of this technique. The distinctive acoustic and rhythmic features found are gathered. Most studies record a diagnostic accuracy over 88% for Alzheimer’s and 80% for mild cognitive impairment. Conclusion: Automatic speech analysis is a promising tool for diagnosing mild cognitive impairment and Alzheimer’s disease. The reported features seem to be indicators of the cognitive changes in older people. The specific features and the cognitive changes involved could be the subject of further research.