335 research outputs found
Spatial Pyramid Encoding with Convex Length Normalization for Text-Independent Speaker Verification
In this paper, we propose a new pooling method called spatial pyramid
encoding (SPE) to generate speaker embeddings for text-independent speaker
verification. We first partition the output feature maps from a deep residual
network (ResNet) into increasingly fine sub-regions and extract speaker
embeddings from each sub-region through a learnable dictionary encoding layer.
These embeddings are concatenated to obtain the final speaker representation.
The SPE layer not only generates a fixed-dimensional speaker embedding for a
variable-length speech segment, but also aggregates the information of feature
distribution from multi-level temporal bins. Furthermore, we apply deep length
normalization by augmenting the loss function with ring loss. By applying ring
loss, the network gradually learns to normalize the speaker embeddings using
model weights themselves while preserving convexity, leading to more robust
speaker embeddings. Experiments on the VoxCeleb1 dataset show that the proposed
system using the SPE layer and ring loss-based deep length normalization
outperforms both i-vector and d-vector baselines.Comment: 5 pages, 2 figures, Interspeech 201
H-VECTORS: Utterance-level Speaker Embedding Using A Hierarchical Attention Model
In this paper, a hierarchical attention network to generate utterance-level
embeddings (H-vectors) for speaker identification is proposed. Since different
parts of an utterance may have different contributions to speaker identities,
the use of hierarchical structure aims to learn speaker related information
locally and globally. In the proposed approach, frame-level encoder and
attention are applied on segments of an input utterance and generate individual
segment vectors. Then, segment level attention is applied on the segment
vectors to construct an utterance representation. To evaluate the effectiveness
of the proposed approach, NIST SRE 2008 Part1 dataset is used for training, and
two datasets, Switchboard Cellular part1 and CallHome American English Speech,
are used to evaluate the quality of extracted utterance embeddings on speaker
identification and verification tasks. In comparison with two baselines,
X-vector, X-vector+Attention, the obtained results show that H-vectors can
achieve a significantly better performance. Furthermore, the extracted
utterance-level embeddings are more discriminative than the two baselines when
mapped into a 2D space using t-SNE
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