  Spoken Language Understanding (SLU) is a critical speech recognition
application and is often deployed on edge devices. Consequently, on-device
processing plays a significant role in the practical implementation of SLU.
This paper focuses on the end-to-end (E2E) SLU model due to its small latency
property, unlike a cascade system, and aims to minimize the computational cost.
We reduce the model size by applying tensor decomposition to the Conformer and
E-Branchformer architectures used in our E2E SLU models. We propose to apply
singular value decomposition to linear layers and the Tucker decomposition to
convolution layers, respectively. We also compare COMP/PARFAC decomposition and
Tensor-Train decomposition to the Tucker decomposition. Since the E2E model is
represented by a single neural network, our tensor decomposition can flexibly
control the number of parameters without changing feature dimensions. On the
STOP dataset, we achieved 70.9% exact match accuracy under the tight constraint
of only 15 million parameters.
