RawNet: Advanced End-to-End Deep Neural Network Using Raw Waveforms for Text-Independent Speaker Verification

Abstract

Recently, direct modeling of raw waveforms using deep neural networks has been widely studied for a number of tasks in audio domains. In speaker verification, however, utilization of raw waveforms is in its preliminary phase, requiring further investigation. In this study, we explore end-to-end deep neural networks that input raw waveforms to improve various aspects: front-end speaker embedding extraction including model architecture, pre-training scheme, additional objective functions, and back-end classification. Adjustment of model architecture using a pre-training scheme can extract speaker embeddings, giving a significant improvement in performance. Additional objective functions simplify the process of extracting speaker embeddings by merging conventional two-phase processes: extracting utterance-level features such as i-vectors or x-vectors and the feature enhancement phase, e.g., linear discriminant analysis. Effective back-end classification models that suit the proposed speaker embedding are also explored. We propose an end-toend system that comprises two deep neural networks, one frontend for utterance-level speaker embedding extraction and the other for back-end classification. Experiments conducted on the VoxCeleb1 dataset demonstrate that the proposed model achieves state-of-the-art performance among systems without data augmentation. The proposed system is also comparable to the state-of-the-art x-vector system that adopts data augmentation.

A simulation study on optimal scores for speaker recognition

“…Note that this likelihood ratio is different from the likelihood ratio of the NL score in Eq. (8). The PLDA LR can be formally represented by:…”

“…Recently, deep learning methods gained much attention and embedding based on deep neural nets (DNN) becomes popular [5,6]. With the efforts from multiple research groups, deep speaker embedding models have been significantly improved by comprehensive architectures [7,8], smart pooling approaches [9][10][11][12], task-oriented objectives [13][14][15][16][17][18], and carefully designed training schemes [19][20][21]. As a result, the deep embedding approach has achieved state-of-theart performance [22].…”

Integrated Replay Spoofing-Aware Text-Independent Speaker Verification

“…Recent advances in deep neural networks (DNNs) have improved the performance of speaker verification (SV) systems, including short-duration and far-field scenarios [1][2][3][4][5]. However, SV systems are known to be vulnerable to various presentation attacks, such as replay attacks, voice conversion, and speech synthesis.…”

Self-Attentive Multi-Layer Aggregation with Feature Recalibration and Deep Length Normalization for Text-Independent Speaker Verification System

“…To address this problem, several studies have applied a pooling layer or temporal average layer to an end-to-end system [2,3]. The second is a speaker embedding-based system [4][5][6][7][8][9][10][11][12][13][14], which generates an input of variable length into a vector of fixed length using a DNN. The generated vector is used as an embedding to represent the speaker.…”