Name
Title | ||
|---|---|---|
Adaptation to New Microphones Using Artificial Neural Networks With Trainable Activation Functions. |
Abstract | ||
|---|---|---|
Model adaptation is a key technique that enables a modern automatic speech recognition (ASR) system to adjust its parameters, using a small amount of enrolment data, to the nuances in the speech spectrum due to microphone mismatch in the training and test data. In this brief, we investigate four different adaptation schemes for connectionist (also known as hybrid) ASR systems that learn microphone... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TNNLS.2016.2550532 | IEEE Transactions on Neural Networks and Learning Systems |
Keywords | Field | DocType |
Microphones,Training,Adaptation models,Data models,Speech recognition,Hidden Markov models,Speech | Data modeling,Computer science,Artificial intelligence,Artificial neural network,Connectionism,Sigmoid function,Pattern recognition,Speech recognition,Test data,Hidden Markov model,Sentence,Machine learning,Microphone | Journal |
Volume | Issue | ISSN |
28 | 8 | 2162-237X |
Citations | PageRank | References |
2 | 0.36 | 13 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sabato Marco Siniscalchi | 1 | 310 | 30.21 |
| Valerio Mario Salerno | 2 | 12 | 2.62 |