Vocabulary and language model adaptation using just one speech file

This paper investigates unsupervised vocabulary and language model self-adaptation (VLA) from just one speech file using the web as a knowledge source and without prior knowledge of topic or domain beyond optional file metadata. Single-file self adaptation is regularly used for acoustic adaptation, but to date, is rarely used for VLA. The method investigated here uses a first-pass transcript or file metadata to generate web search queries for retrieving texts for adaptation. Various strategies for building queries, retrieving web texts and maximizing out-of-vocabulary (OOV) recovery while constraining vocabulary growth are examined. Significant improvements are demonstrated for transcribing and searching recorded lectures and telephone calls. The proposed method is orthogonal with acoustic adaptation and system combination and integrates well in multi-pass recognition architectures.