MUST: A Multilingual Student-Teacher Learning approach for low-resource
  speech recognition
Muhammad Umar Farooq Rehan Ahmad Thomas Hain
Student-teacher learning or knowledge distillation (KD) has been previously
used to address data scarcity issue for training of speech recognition (ASR)
systems. However, a limitation of KD training is that the student model classes
must be a proper or improper subset of the teacher model classes. It prevents
distillation from even acoustically similar languages if the character sets are
not same. In this work, the aforementioned limitation is addressed by proposing
a MUltilingual Student-Teacher (MUST) learning which exploits a posteriors
mapping approach. A pre-trained mapping model is used to map posteriors from a
teacher language to the student language ASR. These mapped posteriors are used
as soft labels for KD learning. Various teacher ensemble schemes are
experimented to train an ASR model for low-resource languages. A model trained
with MUST learning reduces relative character error rate (CER) up to 9.5% in
comparison with a baseline monolingual ASR.