Year: 2007
Type: conference-paper
Abstract: We propose a novel framework to detect and recognize outof-vocabulary (OOV) words in automated speech recognition (ASR).In the proposed framework a hybrid language model combining words and sub-word units is incorporated during ASR decoding then three different OOV words recognition methods are applied to generate OOV word hypotheses.Specifically, dictionary lookup, morphological composition, and direct phoneme-to-grapheme.The proposed approach successfully reduced WER by 1.9% and 1.6% for ASR systems with recognition vocabularies of 30K and 219K.Moreover, the proposed approach correctly recognized 5% of OOV words.
Source: Interspeech (USB)
Institution: Carnegie Mellon University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W9638664