  Deep biasing (DB) improves the performance of end-to-end automatic speech
recognition (E2E-ASR) for rare words or contextual phrases using a bias list.
However, most existing methods treat bias phrases as sequences of subwords in a
predefined static vocabulary, which can result in ineffective learning of the
dependencies between subwords. More advanced techniques address this problem by
incorporating additional text data, which increases the overall workload. This
paper proposes a dynamic vocabulary where phrase-level bias tokens can be added
during the inference phase. Each bias token represents an entire bias phrase
within a single token, thereby eliminating the need to learn the dependencies
between the subwords within the bias phrases. This method can be applied to
various architectures because it only extends the embedding and output layers
in common E2E-ASR architectures. Experimental results demonstrate that the
proposed method improves the performance of bias phrases on English and
Japanese datasets.
