Search Results for author:
Found 9 papers, 3 papers with code
Advancing Audio Emotion and Intent Recognition with Large Pre-Trained Models and Bayesian Inference
In this paper, we employ large pre-trained models for the ACM Multimedia Computational Paralinguistics Challenge, addressing the Requests and Emotion Share tasks.
Topic Identification For Spontaneous Speech: Enriching Audio Features With Embedded Linguistic Information
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.
End-to-end Ensemble-based Feature Selection for Paralinguistics Tasks
The current state-of-the-art methods proposed for these tasks are ensembles based on deep neural networks like ResNets in conjunction with feature engineering.
Comparison and Analysis of New Curriculum Criteria for End-to-End ASR
We hypothesize that end-to-end models can achieve better performance when provided with an organized training set consisting of examples that exhibit an increasing level of difficulty (i. e. a curriculum).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Lahjoita puhetta -- a large-scale corpus of spoken Finnish with some benchmarks
The Donate Speech campaign has so far succeeded in gathering approximately 3600 hours of ordinary, colloquial Finnish speech into the Lahjoita puhetta (Donate Speech) corpus.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Data augmentation using prosody and false starts to recognize non-native children's speech
Firstly, we apply the prosody-based data augmentation to supplement the audio data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Aalto's End-to-End DNN systems for the INTERSPEECH 2020 Computational Paralinguistics Challenge
On ComParE 2020 tasks, we investigate applying an ensemble of E2E models for robust performance and developing task-specific modifications for each task.
Ultrasound-based Silent Speech Interface Built on a Continuous Vocoder
Recently it was shown that within the Silent Speech Interface (SSI) field, the prediction of F0 is possible from Ultrasound Tongue Images (UTI) as the articulatory input, using Deep Neural Networks for articulatory-to-acoustic mapping.
Sound Audio and Speech Processing
GMM-Free Flat Start Sequence-Discriminative DNN Training
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).
