An investigation towards speaker identification using a single-sound-frame

Abstract

Traditional neural network-based speaker identification (SI) studies employ a combination of acoustic features extracted from sequential sounds to present the speakers’ voice biometrics in which several sound segments before and after the current segment are stacked and fed to the network. Although this method is particularly important for speech recognition tasks where words are constructed from sequential sound segments, and successful recognition of words depends on the previous phonetic sequences, SI systems should be able to operate based on the distinctive speaker features available in an individual sound segment and identify the speaker regardless of the previously uttered sounds. This paper investigates this hypothesis by proposing a novel text-independent SI model trained at sound level. In order to achieve this, the investigation was conducted by first studying the best distinguishable configuration of coefficients in a single acoustic segment, then to identify the best frame length to overlapping ratio, and finally measuring the reliability of conducting SI using only a single sound segment. Overall more than one hundred SI systems were trained and evaluated, in which results indicate that performing SI using a single acoustic sound frame decreases the complexity of SI and facilitates it since the classifier requires to learn fewer number of acoustic features in compare to the traditional stacked-based approaches.