Name
Abstract | ||
|---|---|---|
In this paper, we describe our work on the ASpIRE (Automatic Speech recognition In Reverberant Environments) challenge, which aims to assess the robustness of automatic speech recognition (ASR) systems. The main characteristic of the challenge is developing a high-performance system without access to matched training and development data. While the evaluation data are recorded with far-field microphones in noisy and reverberant rooms, the training data are telephone speech and close talking. Our approach to this challenge includes speech enhancement, neural network methods and acoustic model adaptation, We show that these techniques can successfully alleviate the performance degradation due to noisy audio and data mismatch. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ASRU.2015.7404841 | 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) |
Keywords | Field | DocType |
ASpIRE challenge,robust speech recognition | Speech enhancement,Speech processing,Speech coding,Pattern recognition,Computer science,Voice activity detection,Robustness (computer science),Speech recognition,Artificial intelligence,Artificial neural network,Speech technology,Acoustic model | Conference |
Citations | PageRank | References |
3 | 0.39 | 7 |
Authors | ||
14 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Roger Hsiao | 1 | 57 | 3.32 |
| Jeff Z. Ma | 2 | 133 | 15.79 |
| William Hartmann | 3 | 64 | 10.66 |
| Martin Karafiát | 4 | 227 | 23.61 |
| Frantisek Grézl | 5 | 389 | 38.12 |
| Lukas Burget | 6 | 581 | 74.84 |
| Igor Szöke | 7 | 310 | 22.64 |
| Jan Cernocký | 8 | 1273 | 135.94 |
| Shinji Watanabe | 9 | 1158 | 139.38 |
| Zhuo Chen | 10 | 153 | 24.33 |
| Sri Harish Reddy Mallidi | 11 | 48 | 7.94 |
| Hynek Hermansky | 12 | 3298 | 510.27 |
| Stavros Tsakalidis | 13 | 213 | 13.83 |
| Richard M. Schwartz | 14 | 2839 | 717.76 |