VoxCeleb datasets are widely used in speaker recognition studies. Our work serves two purposes. First, we provide speaker age labels and (an alternative) annotation of speaker gender. Second, we demonstrate the use of this metadata by constructing age and gender recognition models with different features and classifiers. We query different celebrity databases and apply consensus rules to derive age and gender labels. We also compare the original VoxCeleb gender labels with our labels to identify records that might be mislabeled in the original VoxCeleb data. On modeling side, we design a comprehensive study of multiple features and models for recognizing gender and age. Our best system, using i-vector features, achieved an F1-score of 0.9829 for gender recognition task using logistic regression, and the lowest mean absolute error (MAE) in age regression, 9.443 years, is obtained with ridge regression. This indicates challenge in age estimation from in-the-wild style speech data.

Bottleneck features (BNFs) generated with a deep neural network (DNN) have proven to boost spoken language recognition accuracy over basic spectral features significantly. However, BNFs are commonly extracted using language-dependent tied-context phone states as learning targets. Moreover, BNFs are less phonetically expressive than the output layer in a DNN, which is usually not used as a speech feature because of its very high dimensionality hindering further post-processing. In this article, we put forth a novel deep learning framework to overcome all of the above issues and evaluate it on the 2017 NIST Language Recognition Evaluation (LRE) challenge. We use manner and place of articulation as speech attributes, which lead to low-dimensional “universal” phonetic features that can be defined across all spoken languages. To model the asynchronous nature of the speech attributes while capturing their intrinsic relationships in a given speech segment, we introduce a new training scheme for deep architectures based on a Maximal Figure of Merit (MFoM) objective. MFoM introduces non-differentiable metrics into the backpropagation-based approach, which is elegantly solved in the proposed framework. The experimental evidence collected on the recent NIST LRE 2017 challenge demonstrates the effectiveness of our solution. In fact, the performance of speech language recognition (SLR) systems based on spectral features is improved for more than 5% absolute Cavg. Finally, the F1 metric can be brought from 77.6% up to 78.1% by combining the conventional baseline phonetic BNFs with the proposed articulatory attribute features.

Deep neural networks (DNN) are able to successfully process and classify speech utterances. However, understanding the reason behind a classification by DNN is difficult. One such debugging method used with image classification DNNs is activation maximization, which generates example-images that are classified as one of the classes. In this work, we evaluate applicability of this method to speech utterance classifiers as the means to understanding what DNN "listens to". We trained a classifier using the speech command corpus and then use activation maximization to pull samples from the trained model. Then we synthesize audio from features using WaveNet vocoder for subjective analysis. We measure the quality of generated samples by objective measurements and crowd-sourced human evaluations. Results show that when combined with the prior of natural speech, activation maximization can be used to generate examples of different classes. Based on these results, activation maximization can be used to start opening up the DNN black-box in speech tasks.

Technical report for NIST LRE 2015 Workshop