PRISM: Pre-trained Indeterminate Speaker Representation Model for Speaker Diarization and Speaker Verification

05/16/2022
by   Siqi Zheng, et al.
0

Speaker embedding has been a fundamental feature for speaker-related tasks such as verification, clustering, and diarization. Traditionally, speaker embeddings are represented as fixed vectors in high-dimensional space. This could lead to biased estimations, especially when handling shorter utterances. In this paper we propose to represent a speaker utterance as "floating" vector whose state is indeterminate without knowing the context. The state of a speaker representation is jointly determined by itself, other speech from the same speaker, as well as other speakers it is being compared to. The content of the speech also contributes to determining the final state of a speaker representation. We pre-train an indeterminate speaker representation model that estimates the state of an utterance based on the context. The pre-trained model can be fine-tuned for downstream tasks such as speaker verification, speaker clustering, and speaker diarization. Substantial improvements are observed across all downstream tasks.

READ FULL TEXT
research
10/12/2021

Large-scale Self-Supervised Speech Representation Learning for Automatic Speaker Verification

The speech representations learned from large-scale unlabeled data have ...
research
04/18/2018

Unspeech: Unsupervised Speech Context Embeddings

We introduce "Unspeech" embeddings, which are based on unsupervised lear...
research
08/17/2022

Disentangled Speaker Representation Learning via Mutual Information Minimization

Domain mismatch problem caused by speaker-unrelated feature has been a m...
research
06/03/2021

MPC-BERT: A Pre-Trained Language Model for Multi-Party Conversation Understanding

Recently, various neural models for multi-party conversation (MPC) have ...
research
09/06/2021

Improving Speaker Identification for Shared Devices by Adapting Embeddings to Speaker Subsets

Speaker identification typically involves three stages. First, a front-e...
research
01/07/2020

Learning Speaker Embedding with Momentum Contrast

Speaker verification can be formulated as a representation learning task...
research
10/25/2022

Improving Speech Representation Learning via Speech-level and Phoneme-level Masking Approach

Recovering the masked speech frames is widely applied in speech represen...

Please sign up or login with your details

Forgot password? Click here to reset