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.
The aforementioned works, depending on the database used to train, achieved performances above 80 Greater performance has been recently achieved, and has been applied in the field of service robotics. For example, in @cite_5 a speaker identification process is carried out by inputting the power spectrum of the speech input, a model is trained for each speaker, and then the Euclidean distance between the models output is measured. The authors report a 96 It is very important to mention that in all the aforementioned works that carry out identification via classification, it is assumed that the users to be identified are known. During the testing phase, new speakers are not added, so it is implied that the models are not able to identify speakers that they were not trained for.
@cite_5
2015068436
This paper presents WISS, a speaker identification system for mobile robots integrated to ManyEars, a sound source localization, tracking and separation system. Speaker identification consists in recognizing an individual among a group of known speakers. For mobile robots, performing speaker identification in presence of noise that changes over time is one important challenge. To deal with this issue, WISS uses Parallel Model Combination (PMC) and masks to update in real-time the speaker models (obtained in clean conditions) to both additive and convolutive noises. The results show that the weighted rate of good speaker identifications is 96 on average for a Signal-to-Noise Ratio (SNR) of 16 dB, whereas it only decreases to 84 when the SNR drops to 2 dB.