Výsledky vyhledávání - "Mahnoosh Mehrabani"

Improved End-To-End Spoken Utterance Classification with a Self-Attention Acoustic Classifier

Autor: Mahnoosh Mehrabani, Ryan Price, Srinivas Bangalore

Publikováno v: ICASSP

While human language provides a natural interface for humanmachine communication, there are several challenges concerning extracting the intents of a speaker when interacting with a virtual agent, especially when the speaker is in a noisy acoustic en

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::38b66364dde055ef06168e31083ad348
https://doi.org/10.1109/icassp40776.2020.9054417

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Singing speaker clustering based on subspace learning in the GMM mean supervector space

Autor: John H. L. Hansen, Mahnoosh Mehrabani

Publikováno v: Speech Communication. 55:653-666

Highlights? Mixed style speech causes problems when training acoustic models for speech applications, such as speaker ID and ASR. ? This study is a first attempt for speaker clustering under mixed speaking styles which include reading and singing. ?

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::af9fde152d3ff8f05ce7fb6e8360053b
https://doi.org/10.1016/j.specom.2012.11.001

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Personalized speech recognition for Internet of Things

Autor: Srinivas Bangalore, Mahnoosh Mehrabani, Benjamin Stern

Publikováno v: WF-IoT

With the rapid increasing of smart devices, there has been a growing interest in the concept of Internet of Things (IoT). While as a network of connected objects, IoT is created by enabling machine to machine interactions, another important factor of

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::cdda19a6ce807ce62673763071aad4a3
https://doi.org/10.1109/wf-iot.2015.7389082

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Emotion clustering based on probabilistic linear discriminant analysis

Autor: Ozlem Kalinli, Mahnoosh Mehrabani, Ruxin Chen

Publikováno v: INTERSPEECH

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::f6cb40730c54a17afaa16dc5d0166593
https://doi.org/10.21437/interspeech.2015-327

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Dimensionality analysis of singing speech based on locality preserving projections

Autor: John H. L. Hansen, Mahnoosh Mehrabani

Publikováno v: INTERSPEECH

In this study, we expand the question of ”what is the intrinsic dimensionality of speech?” to ”how does the intrinsic dimensionality of speech change from speaking to singing?”. Our focus is on dimensionality of the vowel space regarding spec

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::de5cb725528935a244535837ec0e5ecc
https://doi.org/10.21437/interspeech.2013-650

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Unsupervised prominence prediction for speech synthesis

Autor: Taniya Mishra, Mahnoosh Mehrabani, Alistair Conkie

Publikováno v: INTERSPEECH

We propose an unsupervised prominence prediction method for expressive speech synthesis. Prominence patterns are learned by statistical analysis of prosodic features extracted from speech data. The advantages of our unsupervised datadriven prominence

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::a5c2b1ccfa776b5318d776b96e49c1ea
https://doi.org/10.21437/interspeech.2013-394

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Speaker clustering for a mixture of singing and reading

Autor: Mahnoosh Mehrabani, John H. L. Hansen

Publikováno v: Interspeech 2012.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::e91d9ea96f848d8bf82345ffb41d790e
https://doi.org/10.21437/interspeech.2012-593

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Nativeness classification with suprasegmental features on the accent group level

Autor: Mahnoosh Mehrabani, Joseph Tepperman, Emily Nava

Publikováno v: Interspeech 2012.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::c4aaafa46b6f03cf5281f4a24ba4f827
https://doi.org/10.21437/interspeech.2012-553

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Language identification using a combined articulatory prosody framework

Autor: Abhijeet Sangwan, Mahnoosh Mehrabani, John H. L. Hansen

Publikováno v: ICASSP

This study presents new advancements in our articulatory-based language identification (LID) system. Our LID system automatically identifies language-features (LFs) from a phonological features (PFs) based representation of speech. While our baseline

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::766215a0ac538f45f57f91527dd840b3
https://doi.org/10.1109/icassp.2011.5947329

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