Search Results for author:
Found 13 papers, 2 papers with code
Robust Open-Set Spoken Language Identification and the CU MultiLang Dataset
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.
Efficient Ensemble for Multimodal Punctuation Restoration using Time-Delay Neural Network
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
A Transaction Represented with Weighted Finite-State Transducers
Not all contracts are good, but all good contracts can be expressed as a finite-state transition system ("State-Transition Contracts").
Modernizing Open-Set Speech Language Identification
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.
Bi-LSTM Scoring Based Similarity Measurement with Agglomerative Hierarchical Clustering (AHC) for Speaker Diarization
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.
Automatic Spoken Language Identification using a Time-Delay Neural Network
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
Multi-Modal Emotion Detection with Transfer Learning
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.
A New Approach to Accent Recognition and Conversion for Mandarin Chinese
A prototype of an end-to-end accent converter model is also presented.
A Transfer Learning Method for Speech Emotion Recognition from Automatic Speech Recognition
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
Cross Lingual Cross Corpus Speech Emotion Recognition
The majority of existing speech emotion recognition models are trained and evaluated on a single corpus and a single language setting.
Cantonese Automatic Speech Recognition Using Transfer Learning from Mandarin
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
MULTI-MODAL EMOTION RECOGNITION ON IEMOCAP WITH NEURAL NETWORKS.
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.
Multi-Modal Emotion recognition on IEMOCAP Dataset using Deep Learning
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.
