Deep Neural Networks for Automatic Speaker Recognition Do Not Learn Supra-Segmental Temporal Features

While deep neural networks have shown impressive results in automatic speaker recognition and related tasks, it is dissatisfactory how little is understood about what exactly is responsible for these results. Part of the success has been attributed in prior work to their capability to model supra-segmental temporal information (SST), i.e., learn rhythmic-prosodic characteristics of speech in addition to spectral features. In this paper, we (i) present and apply a novel test to quantify to what extent the performance of state-of-the-art neural networks for speaker recognition can be explained by modeling SST; and (ii) present several means to force respective nets to focus more on SST and evaluate their merits. We find that a variety of CNN- and RNN-based neural network architectures for speaker recognition do not model SST to any sufficient degree, even when forced. The results provide a highly relevant basis for impactful future research into better exploitation of the full speech signal and give insights into the inner workings of such networks, enhancing explainability of deep learning for speech technologies.

Reorganization of the auditory-perceptual space across the human vocal range

We analyzed the auditory-perceptual space across a substantial portion of the human vocal range (220-1046 Hz) using multidimensional scaling analysis of cochlea-scaled spectra from 250-ms vowel segments, initially studied in Friedrichs et al. (2017) J. Acoust. Soc. Am. 142 1025-1033. The dataset comprised the vowels /i y e {\o} {\epsilon} a o u/ (N=240) produced by three native German female speakers, encompassing a broad range of their respective voice frequency ranges. The initial study demonstrated that, during a closed-set identification task involving 21 listeners, the point vowels /i a u/ were significantly recognized at fundamental frequencies (fo) nearing 1 kHz, whereas the recognition of other vowels decreased at higher pitches. Building on these findings, our study revealed systematic spectral shifts associated with vowel height and frontness as fo increased, with a notable clustering around /i a u/ above 523 Hz. These observations underscore the pivotal role of spectral shape in vowel perception, illustrating the reliance on acoustic anchors at higher pitches. Furthermore, this study sheds light on the quantal nature of these vowels and their potential impact on language evolution, offering a plausible explanation for their widespread presence in the world's languages.