Multiple Deep Learning Models and Architectures with Different Numbers of States Used to Improve Retrieval Accuracy of Query-by-Example

• Published in: 2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) pp. 1067 - 1071
• Main Authors: Hatakeyama, Kazuki, Nishino, Masahiro, Kojima, Kazunori, Lee, Shi-wook, Itoh, Yoshiaki
• Format: Conference Proceeding
• Language: English
• Published: APSIPA 14.12.2021
• Subjects: Deep learning; Information processing; Maximum likelihood detection; maximum likelihood state sequence; Memory management; Neural networks; Query by example; Speech recognition; Spoken term detection; Text recognition

Studies examining Spoken Term Detection (STD) and Spoken Query STD (SQ-STD) or Query by Example (QbE) using a spoken query have been conducted actively in recent years. When a spoken query is transcribed into a text using an automatic speech recognizer in SQ-STD, some misrecognition leads to retriev...