| Paper ID | SPE-47.5 |
| Paper Title |
ANALYSIS OF X-VECTORS FOR LOW-RESOURCE SPEECH RECOGNITION |
| Authors |
Martin Karafiat, Karel Vesely, Jan "Honza" Cernocky, Brno University of Technology, Czechia; Jan Profant, Jiri Nytra, Miroslav Hlavacek, Tomas Pavlicek, Phonexia.s.r.o., Czechia |
| Session | SPE-47: Speech Recognition 17: Speech Adaptation and Normalization |
| Location | Gather.Town |
| Session Time: | Friday, 11 June, 11:30 - 12:15 |
| Presentation Time: | Friday, 11 June, 11:30 - 12:15 |
| Presentation |
Poster
|
| Topic |
Speech Processing: [SPE-ADAP] Speech Adaptation/Normalization |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
The paper presents a study of usability of x-vectors for adaptation of automatic speech recognition (ASR) systems. X-vectors are Neural Network (NN)-based speaker embeddings recently proposed in speaker recognition (SR). They quickly replaced common i-vectors and became new state-of-the-art technique. Here, the same approach is adopted for ASR with the hope of similar outcome. All experiments were done ASR for the latest IARPA MATERIAL evaluation running on Pashto language. Over 1% absolute improvement was observed with x-vectors over traditional i-vectors, even when the x-vector extractor was not trained on target Pashto data. |
