6 research outputs found
ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification
Current speaker verification techniques rely on a neural network to extract
speaker representations. The successful x-vector architecture is a Time Delay
Neural Network (TDNN) that applies statistics pooling to project
variable-length utterances into fixed-length speaker characterizing embeddings.
In this paper, we propose multiple enhancements to this architecture based on
recent trends in the related fields of face verification and computer vision.
Firstly, the initial frame layers can be restructured into 1-dimensional
Res2Net modules with impactful skip connections. Similarly to SE-ResNet, we
introduce Squeeze-and-Excitation blocks in these modules to explicitly model
channel interdependencies. The SE block expands the temporal context of the
frame layer by rescaling the channels according to global properties of the
recording. Secondly, neural networks are known to learn hierarchical features,
with each layer operating on a different level of complexity. To leverage this
complementary information, we aggregate and propagate features of different
hierarchical levels. Finally, we improve the statistics pooling module with
channel-dependent frame attention. This enables the network to focus on
different subsets of frames during each of the channel's statistics estimation.
The proposed ECAPA-TDNN architecture significantly outperforms state-of-the-art
TDNN based systems on the VoxCeleb test sets and the 2019 VoxCeleb Speaker
Recognition Challenge.Comment: proceedings of INTERSPEECH 202
A Review of Deep Learning Techniques for Speech Processing
The field of speech processing has undergone a transformative shift with the
advent of deep learning. The use of multiple processing layers has enabled the
creation of models capable of extracting intricate features from speech data.
This development has paved the way for unparalleled advancements in speech
recognition, text-to-speech synthesis, automatic speech recognition, and
emotion recognition, propelling the performance of these tasks to unprecedented
heights. The power of deep learning techniques has opened up new avenues for
research and innovation in the field of speech processing, with far-reaching
implications for a range of industries and applications. This review paper
provides a comprehensive overview of the key deep learning models and their
applications in speech-processing tasks. We begin by tracing the evolution of
speech processing research, from early approaches, such as MFCC and HMM, to
more recent advances in deep learning architectures, such as CNNs, RNNs,
transformers, conformers, and diffusion models. We categorize the approaches
and compare their strengths and weaknesses for solving speech-processing tasks.
Furthermore, we extensively cover various speech-processing tasks, datasets,
and benchmarks used in the literature and describe how different deep-learning
networks have been utilized to tackle these tasks. Additionally, we discuss the
challenges and future directions of deep learning in speech processing,
including the need for more parameter-efficient, interpretable models and the
potential of deep learning for multimodal speech processing. By examining the
field's evolution, comparing and contrasting different approaches, and
highlighting future directions and challenges, we hope to inspire further
research in this exciting and rapidly advancing field