Improving recognition of proper nouns in ASR through generating and filtering phonetic transcriptions

The proposed method consists in a process that first extracts phonetic transcriptions, and then iteratively filters them. In order to initialize the process, an alignment dictionary is used to detect word boundaries. A rule-based grapheme-to-phoneme generator (LIA_PHON), a knowledge-based approach (JSM), and a Statistical Machine Translation based system were evaluated for this alignment. As a result, compared to our reference dictionary (BDLEX supplemented by LIA_PHON for missing words) on the ESTER 1 French broadcast news corpus, we were able to significantly decrease the Word Error Rate (WER) on segments of speech with proper nouns, without negatively affecting the WER on the rest of the corpus.