no code implementations • LREC 2022 • Imran Sheikh, Emmanuel Vincent, Irina Illina
Training of LSTM LMs in such limited data scenarios can benefit from alternate uncertain ASR hypotheses, as observed in our recent work.
no code implementations • 18 Dec 2019 • Sri Harsha Dumpala, Imran Sheikh, Rupayan Chakraborty, Sunil Kumar Kopparapu
Naturally introduced perturbations in audio signal, caused by emotional and physical states of the speaker, can significantly degrade the performance of Automatic Speech Recognition (ASR) systems.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • WS 2018 • Imran Sheikh, Sri Harsha Dumpala, Rupayan Chakraborty, Sunil Kumar Kopparapu
Multimodal sentiment classification in practical applications may have to rely on erroneous and imperfect views, namely (a) language transcription from a speech recognizer and (b) under-performing acoustic views.
Automatic Speech Recognition (ASR) General Classification +2
no code implementations • LREC 2016 • Imran Sheikh, Irina Illina, Dominique Fohr
Out-Of-Vocabulary (OOV) words missed by Large Vocabulary Continuous Speech Recognition (LVCSR) systems can be recovered with the help of topic and semantic context of the OOV words captured from a diachronic text corpus.
no code implementations • 17 Nov 2015 • Imran Sheikh, Irina Illina, Dominique Fohr, Georges Linarès
In this paper, we propose two neural network models targeted to retrieve OOV PNs relevant to an audio document: (a) Document level Continuous Bag of Words (D-CBOW), (b) Document level Continuous Bag of Weighted Words (D-CBOW2).