GLORIA

GEOMAR Library Ocean Research Information Access

X

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
  • 1
    Online Resource
    Online Resource
    Strategies for Disseminating Information on Biomedical Research on Autism to Hispanic Parents
    Springer Science and Business Media LLC ; 2016
    In:  Journal of Autism and Developmental Disorders Vol. 46, No. 3 ( 2016-3), p. 1038-1050
    Details
    In: Journal of Autism and Developmental Disorders, Springer Science and Business Media LLC, Vol. 46, No. 3 ( 2016-3), p. 1038-1050
    Type of Medium: Online Resource
    ISSN: 0162-3257 , 1573-3432
    URL: Article
    DOI: 10.1007/s10803-015-2649-5
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2016
    detail.hit.zdb_id: 2016724-6
    SSG: 5,2
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 2
    Online Resource
    Online Resource
    Effects of emotion on different phoneme classes
    Acoustical Society of America (ASA) ; 2004
    In:  The Journal of the Acoustical Society of America Vol. 116, No. 4_Supplement ( 2004-10-01), p. 2481-2481
    Details
    In: The Journal of the Acoustical Society of America, Acoustical Society of America (ASA), Vol. 116, No. 4_Supplement ( 2004-10-01), p. 2481-2481
    Abstract: This study investigates the effects of emotion on different phoneme classes using short-term spectral features. In the research on emotion in speech, most studies have focused on prosodic features of speech. In this study, based on the hypothesis that different emotions have varying effects on the properties of the different speech sounds, we investigate the usefulness of phoneme-class level acoustic modeling for automatic emotion classification. Hidden Markov models (HMM) based on short-term spectral features for five broad phonetic classes are used for this purpose using data obtained from recordings of two actresses. Each speaker produces 211 sentences with four different emotions (neutral, sad, angry, happy). Using the speech material we trained and compared the performances of two sets of HMM classifiers: a generic set of ‘‘emotional speech’’ HMMs (one for each emotion) and a set of broad phonetic-class based HMMs (vowel, glide, nasal, stop, fricative) for each emotion type considered. Comparison of classification results indicates that different phoneme classes were affected differently by emotional change and that the vowel sounds are the most important indicator of emotions in speech. Detailed results and their implications on the underlying speech articulation will be discussed.
    Type of Medium: Online Resource
    ISSN: 0001-4966 , 1520-8524
    URL: Article
    DOI: 10.1121/1.4784911
    RVK:
    EQ 1000
    Language: English
    Publisher: Acoustical Society of America (ASA)
    Publication Date: 2004
    detail.hit.zdb_id: 1461063-2
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 3
    Online Resource
    Online Resource
    Recognition of voice onset time for use in pronunciation modeling
    Acoustical Society of America (ASA) ; 2005
    In:  The Journal of the Acoustical Society of America Vol. 118, No. 3_Supplement ( 2005-09-01), p. 2026-2026
    Details
    In: The Journal of the Acoustical Society of America, Acoustical Society of America (ASA), Vol. 118, No. 3_Supplement ( 2005-09-01), p. 2026-2026
    Abstract: This study examines methods for recognizing native and accented voiceless stops based on voice onset time (VOT). These methods are tested on data from the Tball corpus of early elementary school children, which includes both native English speakers and Spanish speakers learning English, and which is transcribed to highlight pronunciation variation. We examine the English voiceless stop series, which have long VOT and aspiration, and the corresponding voiceless stops in Spanish accented English, which have short VOT and little aspiration. The methods tested are : (1) to train hidden Markov models (HMMs) based on native speech and then extract the VOT times by post-processing phone-level alignments, (2) to train HMMs with explicit aspiration models, and (3) to train, for each phoneme, different HMMs for native and accented variants. Error rates of 23%–53% for distinguishing phone VOT characteristics are reported for the first method, 5%–57% for the second method, and 0%–36% for the third. The error rates varied depending on the different phones examined. In general, the /p/ and /k/ phones had results that varied more than /t/. These results are discussed in light of each method’s usefulness and ease of implementation, and possible improvements are proposed.
    Type of Medium: Online Resource
    ISSN: 0001-4966 , 1520-8524
    URL: Article
    DOI: 10.1121/1.4785771
    RVK:
    EQ 1000
    Language: English
    Publisher: Acoustical Society of America (ASA)
    Publication Date: 2005
    detail.hit.zdb_id: 1461063-2
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 4
    Online Resource
    Online Resource
    Automatically assessing the ABCs : Verification of children's spoken letter-names and letter-sounds
    Association for Computing Machinery (ACM) ; 2011
    In:  ACM Transactions on Speech and Language Processing Vol. 7, No. 4 ( 2011-08), p. 1-17
    Details
    In: ACM Transactions on Speech and Language Processing, Association for Computing Machinery (ACM), Vol. 7, No. 4 ( 2011-08), p. 1-17
    Abstract: Automatic literacy assessment is an area of research that has shown significant progress in recent years. Technology can be used to automatically administer reading tasks and analyze and interpret children's reading skills. It has the potential to transform the classroom dynamic by providing useful information to teachers in a repeatable, consistent, and affordable way. While most previous research has focused on automatically assessing children reading words and sentences, assessments of children's earlier foundational skills is needed. We address this problem in this research by automatically verifying preliterate children's pronunciations of English letter-names and the sounds each letter represents (“letter-sounds”). The children analyzed in this study were from a diverse bilingual background and were recorded in actual kindergarten to second grade classrooms. We first manually verified (accept/reject) the letter-name and letter-sound utterances, which serve as the ground-truth in this study. Next, we investigated four automatic verification methods that were based on automatic speech recognition techniques. We attained percent agreement with human evaluations of 90% and 85% for the letter-name and letter-sound tasks, respectively. Humans agree between themselves an average of 95% of the time for both tasks. We discuss the various confounding factors for this assessment task, such as background noise and the presence of disfluencies, that impact automatic verification performance.
    Type of Medium: Online Resource
    ISSN: 1550-4875 , 1550-4883
    URL: Article
    DOI: 10.1145/1998384.1998389
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2011
    detail.hit.zdb_id: 2178011-0
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 5
    Online Resource
    Online Resource
    Fuzzy Logic Models for the Meaning of Emotion Words
    Institute of Electrical and Electronics Engineers (IEEE) ; 2013
    In:  IEEE Computational Intelligence Magazine Vol. 8, No. 2 ( 2013-05), p. 34-49
    Details
    In: IEEE Computational Intelligence Magazine, Institute of Electrical and Electronics Engineers (IEEE), Vol. 8, No. 2 ( 2013-05), p. 34-49
    Type of Medium: Online Resource
    ISSN: 1556-603X
    URL: Article
    DOI: 10.1109/MCI.2013.2247824
    Language: Unknown
    Publisher: Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2013
    detail.hit.zdb_id: 2209668-1
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 6
    Online Resource
    Online Resource
    Table 7: Observed emotion words from the Mechanical Turk experiment, by difficulty.
    PeerJ ; 2016
    In:  PeerJ Computer Science Vol. 2 ( 2016-01-13), p. e40-
    Details
    In: PeerJ Computer Science, PeerJ, Vol. 2 ( 2016-01-13), p. e40-
    Abstract: We describe and experimentally validate a question-asking framework for machine-learned linguistic knowledge about human emotions. Using the Socratic method as a theoretical inspiration, we develop an experimental method and computational model for computers to learn subjective information about emotions by playing emotion twenty questions (EMO20Q), a game of twenty questions limited to words denoting emotions. Using human–human EMO20Q data we bootstrap a sequential Bayesian model that drives a generalized pushdown automaton-based dialog agent that further learns from 300 human–computer dialogs collected on Amazon Mechanical Turk. The human–human EMO20Q dialogs show the capability of humans to use a large, rich, subjective vocabulary of emotion words. Training on successive batches of human–computer EMO20Q dialogs shows that the automated agent is able to learn from subsequent human–computer interactions. Our results show that the training procedure enables the agent to learn a large set of emotion words. The fully trained agent successfully completes EMO20Q at 67% of human performance and 30% better than the bootstrapped agent. Even when the agent fails to guess the human opponent’s emotion word in the EMO20Q game, the agent’s behavior of searching for knowledge makes it appear human-like, which enables the agent to maintain user engagement and learn new, out-of-vocabulary words. These results lead us to conclude that the question-asking methodology and its implementation as a sequential Bayes pushdown automaton are a successful model for the cognitive abilities involved in learning, retrieving, and using emotion words by an automated agent in a dialog setting.
    Type of Medium: Online Resource
    ISSN: 2376-5992
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    DOI: 10.7717/peerj-cs.40
    DOI: 10.7717/peerj-cs.40/fig-1
    DOI: 10.7717/peerj-cs.40/fig-2
    DOI: 10.7717/peerj-cs.40/table-1
    DOI: 10.7717/peerj-cs.40/table-2
    DOI: 10.7717/peerj-cs.40/table-3
    DOI: 10.7717/peerj-cs.40/table-4
    DOI: 10.7717/peerj-cs.40/table-5
    DOI: 10.7717/peerj-cs.40/table-6
    DOI: 10.7717/peerj-cs.40/table-7
    Language: English
    Publisher: PeerJ
    Publication Date: 2016
    detail.hit.zdb_id: 2868384-5
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 7
    Online Resource
    Online Resource
    Some articulatory details of emotional speech
    Acoustical Society of America (ASA) ; 2005
    In:  The Journal of the Acoustical Society of America Vol. 118, No. 3_Supplement ( 2005-09-01), p. 2025-2025
    Details
    In: The Journal of the Acoustical Society of America, Acoustical Society of America (ASA), Vol. 118, No. 3_Supplement ( 2005-09-01), p. 2025-2025
    Abstract: Differences in speech articulation among four emotion types, neutral, anger, sadness, and happiness are investigated by analyzing tongue tip, jaw, and lip movement data collected from one male and one female speaker of American English. The data were collected using an electromagnetic articulography (EMA) system while subjects produce simulated emotional speech. Pitch, root-mean-square (rms) energy and the first three formants were estimated for vowel segments. For both speakers, angry speech exhibited the largest rms energy and largest articulatory activity in terms of displacement range and movement speed. Happy speech is characterized by largest pitch variability. It has higher rms energy than neutral speech but articulatory activity is rather comparable to, or less than, neutral speech. That is, happy speech is more prominent in voicing activity than in articulation. Sad speech exhibits longest sentence duration and lower rms energy. However, its articulatory activity is no less than neutral speech. Interestingly, for the male speaker, articulation for vowels in sad speech is consistently more peripheral (i.e., more forwarded displacements) when compared to other emotions. However, this does not hold for female subject. These and other results will be discussed in detail with associated acoustics and perceived emotional qualities. [Work supported by NIH.]
    Type of Medium: Online Resource
    ISSN: 0001-4966 , 1520-8524
    URL: Article
    DOI: 10.1121/1.4809086
    RVK:
    EQ 1000
    Language: English
    Publisher: Acoustical Society of America (ASA)
    Publication Date: 2005
    detail.hit.zdb_id: 1461063-2
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 8
    Online Resource
    Online Resource
    IEMOCAP: interactive emotional dyadic motion capture database
    Springer Science and Business Media LLC ; 2008
    In:  Language Resources and Evaluation Vol. 42, No. 4 ( 2008-12), p. 335-359
    Details
    In: Language Resources and Evaluation, Springer Science and Business Media LLC, Vol. 42, No. 4 ( 2008-12), p. 335-359
    Type of Medium: Online Resource
    ISSN: 1574-020X , 1574-0218
    URL: Article
    DOI: 10.1007/s10579-008-9076-6
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2008
    detail.hit.zdb_id: 2195235-8
    SSG: 24
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...