Results 121 to 130 of about 457 (166)

Topic-Aware Summarization of Lived Health Care Experiences: Large Language Model Evaluation Study. [PDF]

JMIR Med Inform
Bilalpur M, Hamm ME, Lee YJ, Norman NG, Mctigue KM, Wang Y.   +5 more
europepmc   +1 more source

Temporal dynamics of early child-clinician prosodic synchrony predict one year autism intervention outcomes using AI driven affective computing. [PDF]

Sci Rep
Bertamini G, Perzolli S, Bentenuto A, Furlanello C, Chetouani M, Venuti P, Cohen D.   +6 more
europepmc   +1 more source

Building for speech: designing the next-generation of social robots for audio interaction. [PDF]

Front Robot AI
Addlesee A, Papaioannou I.
europepmc   +1 more source

Collecting language, speech acoustics, and facial expression to predict psychosis and other clinical outcomes: strategies from the AMP® SCZ initiative. [PDF]

Schizophrenia (Heidelb)
Bilgrami ZR, Castro E, Agurto C, Liebenthal E, Ennis M, Baker JT, Scott I, Colton BL, Cho KIK, Li L, Tamayo Z, Henecks M, Rahimi Eichi H, Henry T, Addington J, Alameda LK, Arango C, Breitborde NJK, Broome MR, Cadenhead KS, Calkins ME, Chen EYH, Choi J, Conus P, Cornblatt BA, Ellman LM, Fusar-Poli P, Gaspar PA, Gerber C, Glenthøj LB, Horton LE, Hui C, Kambeitz J, Kambeitz-Ilankovic L, Keshavan MS, Kim SW, Koutsouleris N, Kwon JS, Langbein K, Mamah D, Diaz-Caneja CM, Mathalon DH, Mittal VA, Nordentoft M, Pearlson GD, Perez J, Perkins DO, Powers AR, Rogers J, Sabb FW, Schiffman J, Shah JL, Silverstein SM, Smesny S, Stone WS, Yassin W, Strauss GP, Thompson JL, Upthegrove R, Verma S, Wang J, Wolf DH, McGorry PD, Kahn RS, Kane JM, Anticevic A, Bearden CE, Dwyer D, Billah T, Bouix S, Pasternak O, Shenton ME, Woods SW, Nelson B, Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ), Cecchi GA, Corcoran CM, Wolff PM.   +77 more
europepmc   +1 more source

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Multimodal Speaker Diarization

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012
We present a novel probabilistic framework that fuses information coming from the audio and video modality to perform speaker diarization. The proposed framework is a Dynamic Bayesian Network (DBN) that is an extension of a factorial Hidden Markov Model (fHMM) and models the people appearing in an audiovisual recording as multimodal entities that ...
Athanasios K. Noulas, Gwenn Englebienne, Ben J. A. Kröse   +2 more
openaire   +3 more sources

Speaker Diarization in Vietnamese Voice

2021
Speaker diarization is the process of partitioning an input audio stream into homogeneous segments according to different speakers. It is an important process to support speaker recognition systems and identify a speaker in broadcasts, meeting recordings, and voice mail.
Nguyen Duc Nam, Hieu Trung Huynh
openaire   +1 more source

Speaker diarization in meeting audio

2009 IEEE International Conference on Acoustics, Speech and Signal Processing, 2009
This paper describes speaker diarization system on a NIST Rich Transcription 2007 (RT-07) Meeting Recognition evaluation data set for the task of Multiple Distant Microphone (MDM). Our implementation includes three components: initial clustering, non-speech removal and cluster purification.
Tin Lay Nwe, Hanwu Sun, Haizhou Li 0001, Susanto Rahardja   +3 more
openaire   +1 more source

diaLogic: Interaction-Focused Speaker Diarization

2021 IEEE International Systems Conference (SysCon), 2021
diaLogic is a user-friendly Python program which performs social interaction classification through speaker diarization. The main libraries used include Python’s PyQt5 and Keras APIs, Matplotlib, and the computational R language. Speaker diarization is achieved with high consistency due to a simple four-layer convolutional neural network (CNN) trained ...
Ryan Duke, Alex Doboli
openaire   +1 more source