Approaches to Improving Recognition of Underrepresented Named Entities in Hybrid ASR Systems
Table 3: WER (%) and NE-WER (%) performances for different ASR systems with phonetic (Ph) and graphemic (Gr) lexicons
['No.', 'System', 'WER testman', 'WER testman', 'WER testsge', 'WER testsge', 'WER SG-streets', 'WER SG-streets', 'NE-WER SG-streets', 'NE-WER SG-streets', 'NE-WER SG-streets', 'NE-WER SG-streets', 'NE-WER SG-streets', 'NE-WER SG-streets']
The obtained results show that both lexicons achieve comparable WER and NE-WER on all evaluation sets. Given that construction of graphemic lexicon is much easier, this observation is crucial for the development of hybrid ASR systems for new languages. To further study the implications of graphemic lexicon, we expanded our baseline lexicon using OOV words from SG-streets set and re-build the baseline ASR. In total, 506 unique OOV tokens were added, resulting in expanded lexicon covering 16,851 unique tokens. To expand phonetic lexicon, we first trained G2P model using the baseline lexicon and then applied it to generate pronunciations for OOV words. The graphemic lexicon was expanded by simply adding the written forms of OOV words.