5 citations · 10 across the 4 of their papers we have counts for
4 papers
Advancing Audio Emotion and Intent Recognition with Large Pre-Trained Models and Bayesian Inference
Dejan Porjazovski, Yaroslav Getman, Tamás Grósz +1
Large pre-trained models are essential in paralinguistic systems, demonstrating effectiveness in tasks like emotion recognition and stuttering detection. In this paper, we employ l…
Topic Identification For Spontaneous Speech: Enriching Audio Features With Embedded Linguistic Information
Dejan Porjazovski, Tamás Grósz, Mikko Kurimo
Traditional topic identification solutions from audio rely on an automatic speech recognition system (ASR) to produce transcripts used as input to a text-based model. These approac…
Comparison and Analysis of New Curriculum Criteria for End-to-End ASR
Georgios Karakasidis, Tamás Grósz, Mikko Kurimo
It is common knowledge that the quantity and quality of the training data play a significant role in the creation of a good machine learning model. In this paper, we take it one st…
GMM-Free Flat Start Sequence-Discriminative DNN Training
Gábor Gosztolya, Tamás Grósz, László Tóth
Recently, attempts have been made to remove Gaussian mixture models (GMM) from the training process of deep neural network-based hidden Markov models (HMM/DNN). For the GMM-free tr…