Towards directly modeling raw speech signal for speaker verification using CNNs

Speaker verification systems traditionally extract and model cepstral features or filter bank energies from the speech signal. In this paper, inspired by the success of neural network-based approaches to model directly raw speech signal for applications such as speech recognition, emotion recognition and anti-spoofing, we propose a speaker verification approach where speaker discriminative information is directly learned from the speech signal by: (a) first training a CNN-based speaker identification system that takes as input raw speech signal and learns to classify on speakers (unknown to the speaker verification system); and then (b) building a speaker detector for each speaker in the speaker verification system by replacing the output layer of the speaker identification system by two outputs (genuine, impostor), and adapting the system in a discriminative manner with enrollment speech of the speaker and impostor speech data. Our investigations on the Voxforge database shows that this approach can yield systems competitive to state-of-the-art systems. An analysis of the filters in the first convolution layer shows that the filters give emphasis to information in low frequency regions (below 1000 Hz) and implicitly learn to model fundamental frequency information in the speech signal for speaker discrimination.

On Learning to Identify Genders from Raw Speech Signal Using CNNs

Selen Hande Kabil, Hannah Muckenhirn

Automatic Gender Recognition (AGR) is the task of identifying the gender of a speaker given a speech signal. Standard approaches extract features like fundamental frequency and cepstral features from the speech signal and train a binary classifier. Inspired from recent works in the area of automatic speech recognition (ASR), speaker recognition and presentation attack detection, we present a novel approach where relevant features and classifier are jointly learned from the raw speech signal in end-to-end manner. We propose a convolutional neural networks (CNN) based gender classifier that consists of: (1) convolution layers, which can be interpreted as a feature learning stage and (2) a multilayer perceptron (MLP), which can be interpreted as a classification stage. The system takes raw speech signal as input, and outputs gender posterior probabilities. Experimental studies conducted on two datasets, namely AVspoof and ASVspoof 2015, with different architectures show that with simple architectures the proposed approach yields better system than standard acoustic features based approach. Further analysis of the CNNs show that the CNNs learn formant and fundamental frequency information for gender identification.

ISCA-INT SPEECH COMMUNICATION ASSOC2018

Boosting Localized Features for Speaker and Speech Recognition

Anindya Roy

In this thesis, we propose a novel approach for speaker and speech recognition involving localized, binary, data-driven features. The proposed approach is largely inspired by similar localized approaches in the computer vision domain. The success of these existing approaches coupled with their proven advantages of robustness and computational efficiency motivated us to apply these ideas to the speech domain. Our approach is distinct from the standard cepstral features-based approach for speaker and speech recognition. The proposed approach starts with a large set of simple localized features, each of which looks at very small parts of spectro-temporal representations of speech. Each feature is binary-valued. The most discriminative of these features are selected by boosting and combined to form the final classifier. Two systems are developed based on this general framework, a speaker recognition system and a speech recognition system. The speaker recognition system is evaluated under a wide range of experimental conditions, using clean speech, noisy speech and speech data collected from mobile phones. The system performs reliably in each condition, comparable with the standard systems using cepstral features and Gaussian Mixture Models. At the same time, it involves significantly lower number of floating point operations compared to these systems. In the case of the speech recognition system, we integrate our localized features with a Hidden Markov Model framework using multilayer perceptrons. Continuous speech recognition studies on standard databases show that these features perform equally well as cepstral features. It is also found that the fusion of these features with cepstral features leads to improved performance at both the feature level and the decision level. Apart from this, minor contributions include an audio-visual person recognition system developed using the same general approach of localized features described above, extending its applicability. Finally, a new (but related) class of localized features was developed for robust face detection.

EPFL2011

Improving Object Classification using Pose Information

Ronan Collobert, François Fleuret, David Grangier, Hugo Augusto Penedones Fernandes

We propose a method that exploits pose information in order to improve object classification. A lot of research has focused in other strategies, such as engineering feature extractors, trying different classifiers and even using transfer learning. Here, we use neural network architectures in a multi-task setup, whose outputs predict both the class and the camera azimuth. We investigate both Multi-layer Perceptrons and Convolutional Neural Network architectures, and achieve state-of-the-art results in the challenging NORB dataset.

Idiap2012

Boosting Localized Features for Speaker and Speech Recognition

Anindya Roy

EPFL2011