Whispered Speech Recognition Using Deep Denoising Autoencoder and Inverse Filtering

Due to the profound differences between acoustic characteristics of neutral and whispered speech, the performance of traditional automatic speech recognition (ASR) systems trained on neutral speech degrades significantly when whisper is applied. In order to deeply analyze this mismatched train/test...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	IEEE/ACM transactions on audio, speech, and language processing Jg. 25; H. 12; S. 2313 - 2322
Hauptverfasser:	Grozdic, Dorde T., Jovicic, Slobodan T.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	IEEE 01.12.2017
Schlagworte:	Automatic speech recognition Biology Character recognition deep denoising autoencoder Encoding Inverse filtering Mel frequency cepstral coefficient Noise levels Speech recognition Teager-energy operator whispered speech recognition
ISSN:	2329-9290, 2329-9304
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!