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.
There is a vast amount of literature on speaker identification, with two important types of techniques: by classification and by verification. The classification type of techniques train one model with an output limited by the classes (or, in this case, known speakers) that it was trained with. When a new speaker enters the scenario, the model attempts to match it with one of the known speakers, resulting in a false positive. On the other hand, the verification type of techniques train a model for each known speaker, with their outputs providing the probability of the speaker being the one with which the model was trained for. This type of techniques are able to register if a speaker is unknown (when all the models provide low probabilities), however, they generally tend to be less accurate than the classification type of techniques @cite_11 .
@cite_11
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A tutorial on the design and development of automatic speaker-recognition systems is presented. Automatic speaker recognition is the use of a machine to recognize a person from a spoken phrase. These systems can operate in two modes: to identify a particular person or to verify a person's claimed identity. Speech processing and the basic components of automatic speaker-recognition systems are shown and design tradeoffs are discussed. Then, a new automatic speaker-recognition system is given. This recognizer performs with 98.9 correct decalcification. Last, the performances of various systems are compared.