Latent Space Representation For Multi-target Speaker Detection And Identification With A Sparse Dataset Using Triplet Neural Networks
2019 · Kin Wai Cheuk, Balamurali B. T., Gemma Roig, et al.
Abstract
We present an approach to tackle the speaker recognition problem using Triplet Neural Networks. Currently, the \(i\)-vector representation with probabilistic linear discriminant analysis (PLDA) is the most commonly used technique to solve this problem, due to high classification accuracy with a relatively short computation time. In this paper, we explore a neural network approach, namely Triplet Neural Networks (TNNs), to built a latent space for different classifiers to solve the Multi-Target Speaker Detection and Identification Challenge Evaluation 2018 (MCE 2018) dataset. This training set contains \(i\)-vectors from 3,631 speakers, with only 3 samples for each speaker, thus making speaker recognition a challenging task. When using the train and development set for training both the TNN and baseline model (i.e., similarity evaluation directly on the \(i\)-vector representation), our proposed model outperforms the baseline by 23%. When reducing the training data to only using the tra
Authors
(none)
Tags
Stats
Related papers
- Triplet Network With Attention For Speaker Diarization (2018)7.16
- Triplet Based Embedding Distance And Similarity Learning For Text-independent Speaker Verification (2019)5.24
- Titanet: Neural Model For Speaker Representation With 1D Depth-wise Separable Convolutions And Global Context (2021)14.90
- Deep Neural Network Based I-vector Mapping For Speaker Verification Using Short Utterances (2018)0.00
- End-to-end DNN Based Speaker Recognition Inspired By I-vector And PLDA (2017)10.35
- Triplet Entropy Loss: Improving The Generalisation Of Short Speech Language Identification Systems (2020)0.00
- Multi-target Extractor And Detector For Unknown-number Speaker Diarization (2022)8.09
- Triplet Loss Based Embeddings For Forensic Speaker Identification In Spanish (2021)2.26