Using Text Injection to Improve Recognition of Personal Identifiers in
  Speech
Yochai Blau Rohan Agrawal Lior Madmony Gary Wang Andrew Rosenberg Zhehuai Chen Zorik Gekhman Genady Beryozkin Parisa Haghani Bhuvana Ramabhadran
Accurate recognition of specific categories, such as persons' names, dates or
other identifiers is critical in many Automatic Speech Recognition (ASR)
applications. As these categories represent personal information, ethical use
of this data including collection, transcription, training and evaluation
demands special care. One way of ensuring the security and privacy of
individuals is to redact or eliminate Personally Identifiable Information (PII)
from collection altogether. However, this results in ASR models that tend to
have lower recognition accuracy of these categories. We use text-injection to
improve the recognition of PII categories by including fake textual substitutes
of PII categories in the training data using a text injection method. We
demonstrate substantial improvement to Recall of Names and Dates in medical
notes while improving overall WER. For alphanumeric digit sequences we show
improvements to Character Error Rate and Sentence Accuracy.