no code implementations • 29 Aug 2023 • Mustafa Eyceoz, Justin Lee, Siddharth Pittie, Homayoon Beigi
Most state-of-the-art spoken language identification models are closed-set; in other words, they can only output a language label from the set of classes they were trained on.
1 code implementation • 26 Feb 2023 • Xing Yi Liu, Homayoon Beigi
Punctuation restoration plays an essential role in the post-processing procedure of automatic speech recognition, but model efficiency is a key requirement for this task.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3
no code implementations • 1 Feb 2023 • J. Nathaniel Holmes, Homayoon Beigi
Not all contracts are good, but all good contracts can be expressed as a finite-state transition system ("State-Transition Contracts").
no code implementations • 20 May 2022 • Mustafa Eyceoz, Justin Lee, Homayoon Beigi
While most modern speech Language Identification methods are closed-set, we want to see if they can be modified and adapted for the open-set problem.
no code implementations • 19 May 2022 • Siddharth S. Nijhawan, Homayoon Beigi
However, to identify speaker through clustering, models depend on methodologies like PLDA to generate similarity measure between two extracted segments from a given conversational audio.
no code implementations • 19 May 2022 • Benjamin Kepecs, Homayoon Beigi
Closed-set spoken language identification is the task of recognizing the language being spoken in a recorded audio clip from a set of known languages.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +4
no code implementations • 13 Nov 2020 • Amith Ananthram, Kailash Karthik Saravanakumar, Jessica Huynh, Homayoon Beigi
To address these two challenges, we present a multi-modal approach that first transfers learning from related tasks in speech and text to produce robust neural embeddings and then uses these embeddings to train a pLDA classifier that is able to adapt to previously unseen emotions and domains.
no code implementations • 7 Aug 2020 • Lin Ai, Shih-Ying Jeng, Homayoon Beigi
A prototype of an end-to-end accent converter model is also presented.
no code implementations • 6 Aug 2020 • Sitong Zhou, Homayoon Beigi
The proposed method resolves this problem by applying transfer learning techniques in order to leverage data from the automatic speech recognition (ASR) task for which ample data is available.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3
no code implementations • 18 Mar 2020 • Shivali Goel, Homayoon Beigi
The majority of existing speech emotion recognition models are trained and evaluated on a single corpus and a single language setting.
no code implementations • 21 Nov 2019 • Bryan Li, Xinyue Wang, Homayoon Beigi
We propose a system to develop a basic automatic speech recognizer(ASR) for Cantonese, a low-resource language, through transfer learning of Mandarin, a high-resource language.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • cs.AI 2018 • Samarth Tripathi, Homayoon Beigi
Emotion recognition has become an important field of re- search in Human Computer Interactions and there is a grow- ing need for automatic emotion recognition systems.
2 code implementations • 16 Apr 2018 • Samarth Tripathi, Sarthak Tripathi, Homayoon Beigi
Emotion recognition has become an important field of research in Human Computer Interactions as we improve upon the techniques for modelling the various aspects of behaviour.