  In Uyghur speech, consonant and vowel reduction are often encountered,
especially in spontaneous speech with high speech rate, which will cause a
degradation of speech recognition performance. To solve this problem, we
propose an effective phone mask training method for Conformer-based Uyghur
end-to-end (E2E) speech recognition. The idea is to randomly mask off a certain
percentage features of phones during model training, which simulates the above
verbal phenomena and facilitates E2E model to learn more contextual
information. According to experiments, the above issues can be greatly
alleviated. In addition, deep investigations are carried out into different
units in masking, which shows the effectiveness of our proposed masking unit.
We also further study the masking method and optimize filling strategy of phone
mask. Finally, compared with Conformer-based E2E baseline without mask
training, our model demonstrates about 5.51% relative Word Error Rate (WER)
reduction on reading speech and 12.92% on spontaneous speech, respectively. The
above approach has also been verified on test-set of open-source data THUYG-20,
which shows 20% relative improvements.
