Recurrent Out-of-Vocabulary Word Detection Using Distribution of Features

Taichi Asami, Ryo Masumura, Yushi Aono, Koichi Shinoda

The repeated use of out-of-vocabulary (OOV) words in a spoken document seriously degrades a speech recognizer’s performance. This paper provides a novel method for accurately detecting such recurrent OOV words. Standard OOV word detection methods classify each word segment into in-vocabulary (IV) or OOV. This word-by-word classification tends to be affected by sudden vocal irregularities in spontaneous speech, triggering false alarms. To avoid this sensitivity to the irregularities, our proposal focuses on consistency of the repeated occurrence of OOV words. The proposed method preliminarily detects recurrent segments, segments that contain the same word, in a spoken document by open vocabulary spoken term discovery using a phoneme recognizer. If the recurrent segments are OOV words, features for OOV detection in those segments should exhibit consistency. We capture this consistency by using the mean and variance (distribution) of features (DOF) derived from the recurrent segments, and use the DOF for IV/OOV classification. Experiments illustrate that the proposed method’s use of the DOF significantly improves its performance in recurrent OOV word detection.

DOI: 10.21437/Interspeech.2016-562

Cite as

Asami, T., Masumura, R., Aono, Y., Shinoda, K. (2016) Recurrent Out-of-Vocabulary Word Detection Using Distribution of Features. Proc. Interspeech 2016, 1320-1324.

author={Taichi Asami and Ryo Masumura and Yushi Aono and Koichi Shinoda},
title={Recurrent Out-of-Vocabulary Word Detection Using Distribution of Features},
booktitle={Interspeech 2016},