Spoken Term Detection and Relevance Score Estimation using Dot-Product of Pronunciation Embeddings

The paper describes a novel approach to Spoken Term Detection (STD) in large spoken archives using deep LSTM networks. The work is based on the previous approach of using Siamese neural networks for STD and naturally extends it to directly localize a spoken term and estimate its relevance score. The...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	arXiv.org 2022-10
Hauptverfasser:	Švec, Jan, Šmídl, Luboš, Psutka, Josef V, Pražák, Aleš
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Computation and Language Computer Science - Sound Confusion Embedding Neural networks Phonemes
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!