| Presentation # | 9 |
| Session: | Detection, Paralinguistics and Coding |
| Session Time: | Wednesday, December 19, 13:30 - 15:30 |
| Presentation Time: | Wednesday, December 19, 13:30 - 15:30 |
| Presentation: |
Poster
|
| Topic: |
Speech recognition and synthesis: |
| Paper Title: |
LSTM-BASED WHISPER DETECTION |
| Authors: |
Zeynab Raeesy; Amazon | | |
| | Kellen Gillespie; Amazon | | |
| | Chengyuan Ma; Amazon | | |
| | Thomas Drugman; Amazon | | |
| | Jiacheng Gu; Amazon | | |
| | Roland Maas; Amazon | | |
| | Ariya Rastrow; Amazon | | |
| | Björn Hoffmeister; Amazon | | |
| Abstract: |
This article presents a whisper speech detector in the far-field domain. The proposed system consists of a long-short term memory (LSTM) neural network trained on log-filterbank energy (LFBE) acoustic features. This model is trained and evaluated on recordings of human interactions with voice- controlled, far-field devices in whisper and normal phonation modes. We compare multiple inference approaches for utterance-level classification by examining trajectories of the LSTM posteriors. In addition, we engineer a set of features based on the signal characteristics inherent to whisper speech, and evaluate their effectiveness in further separating whisper from normal speech. A benchmarking of these features using multilayer perceptrons (MLP) and LSTMs suggests that the proposed features, in combination with LFBE features, can help us further improve our classifiers. We prove that, with enough data, the LSTM model is indeed as capable of learn- ing whisper characteristics from LFBE features alone com- pared to a simpler MLP model that uses both LFBE and features engineered for separating whisper and normal speech. In addition, we prove that the LSTM classifiers accuracy can be further improved with the incorporation of the proposed engineered features. |