Title :
But ASR system for BABEL Surprise evaluation 2014
Author :
Karafiat, Martin ; Vesely, Karel ; Szoke, Igor ; Burget, Lukas ; Grezl, Frantisek ; Hannemann, Mirko ; Cernocky, Jan
Author_Institution :
IT4I Center of Excellence, Brno Univ. of Technol., Brno, Czech Republic
Abstract :
The paper describes Brno University of Technology (BUT) ASR system for 2014 BABEL Surprise language evaluation (Tamil). While being largely based on our previous work, two original contributions were brought: (1) speaker-adapted bottle-neck neural network (BN) features were investigated as an input to DNN recognizer and semi-supervised training was found effective. (2) Adding of noise to training data outperformed a classical de-noising technique while dealing with noisy test data was found beneficial, and the performance of this approach was verified on a relatively clean training/test data setup from a different language. All results are reported on BABEL 2014 Tamil data.
Keywords :
learning (artificial intelligence); neural nets; signal denoising; speaker recognition; ASR system; BABEL surprise language evaluation; BN features; DNN recognizer; classical denoising technique; deep neural networks; noisy test data; semisupervised training; speaker-adapted bottleneck neural network; Abstracts; Artificial neural networks; Feature extraction; Hidden Markov models; Laboratories; Noise measurement; Training; adaptation of neural networks; bottle-neck neural networks; deep neural networks; discriminative training; noisy speech; speech recognition;
Conference_Titel :
Spoken Language Technology Workshop (SLT), 2014 IEEE
DOI :
10.1109/SLT.2014.7078625
