  In service robotics, there is an interest to identify the user by voice
alone. However, in application scenarios where a service robot acts as a waiter
or a store clerk, new users are expected to enter the environment frequently.
Typically, speaker identification models need to be retrained when this occurs,
which can take an impractical amount of time. In this paper, a new approach for
speaker identification through verification has been developed using a Siamese
Convolutional Neural Network architecture (SCNN), where it learns to
generically verify if two audio signals are from the same speaker. By having an
external database of recorded audio of the users, identification is carried out
by verifying the speech input with each of its entries. If new users are
encountered, it is only required to add their recorded audio to the external
database to be able to be identified, without retraining. The system was
evaluated in four different aspects: the performance of the verifier, the
performance of the system as a classifier using clean audio, its speed, and its
accuracy in real-life settings. Its performance in conjunction with its
one-shot-learning capabilities, makes the proposed system a viable alternative
for speaker identification for service robots.
