Name
Title | ||
|---|---|---|
Using bidirectional lstm recurrent neural networks to learn high-level abstractions of sequential features for automated scoring of non-native spontaneous speech |
Abstract | ||
|---|---|---|
We introduce a new method to grade non-native spoken language tests automatically. Traditional automated response grading approaches use manually engineered time-aggregated features (such as mean length of pauses). We propose to incorporate general time-sequence features (such as pitch) which preserve more information than time-aggregated features and do not require human effort to design. We use a type of recurrent neural network to jointly optimize the learning of high level abstractions from time-sequence features with the time-aggregated features. We first automatically learn high level abstractions from time-sequence features with a Bidirectional Long Short Term Memory (BLSTM) and then combine the high level abstractions with time-aggregated features in a Multilayer Perceptron (MLP)/Linear Regression (LR). We optimize the BLSTM and the MLP/LR jointly. We find such models reach the best performance in terms of correlation with human raters. We also find that when there are limited time-aggregated features available, our model that incorporates time-sequence features improves performance drastically. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ASRU.2015.7404814 | 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) |
Keywords | Field | DocType |
Automatic speech scoring,non-native speech,recurrent neural networks | Abstraction,Computer science,Recurrent neural network,Long short term memory,Speech recognition,Time delay neural network,Multilayer perceptron,Correlation,Artificial intelligence,Machine learning,Spoken language,Linear regression | Conference |
Citations | PageRank | References |
8 | 0.58 | 12 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| zhou yu | 1 | 56 | 9.94 |
| Vikram Ramanarayanan | 2 | 70 | 13.97 |
| David Suendermann-Oeft | 3 | 9 | 2.63 |
| Xinhao Wang | 4 | 57 | 15.23 |
| Klaus Zechner | 5 | 534 | 66.55 |
| Lei Chen | 6 | 84 | 7.63 |
| Jidong Tao | 7 | 10 | 1.64 |
| Aliaksei Ivanou | 8 | 8 | 0.58 |
| Qian Yao | 9 | 527 | 51.55 |