arXiv subjects

Soo-Whan Chung

Publications and source records attributed to Soo-Whan Chung.

24 records · Page 2Linked to original sources

arXiv2020

MIRNet: Learning multiple identities representations in overlapped speech

Many approaches can derive information about a single speaker's identity from the speech by learning to recognize consistent characteristics of acoustic parameters. However, it is challenging to determine identity information when there are multiple concurrent speakers in a given signal. In this paper, we propose a novel deep speaker representation strategy that can reliably extract multiple speaker identities from an overlapped speech. We design a network that can extract a high-level embedding that contains information about each speaker's identity from a given mixture. Unlike conventional approaches that need reference acoustic features for training, our proposed algorithm only requires the speaker identity labels of the overlapped speech segments. We demonstrate the effectiveness and usefulness of our algorithm in a speaker verification task and a speech separation system conditioned on the target speaker embeddings obtained through the proposed method.

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arXiv2020

Intra-class variation reduction of speaker representation in disentanglement framework

In this paper, we propose an effective training strategy to ex-tract robust speaker representations from a speech signal. Oneof the key challenges in speaker recognition tasks is to learnlatent representations or embeddings containing solely speakercharacteristic information in order to be robust in terms of intra-speaker variations. By modifying the network architecture togenerate both speaker-related and speaker-unrelated representa-tions, we exploit a learning criterion which minimizes the mu-tual information between these disentangled embeddings. Wealso introduce an identity change loss criterion which utilizes areconstruction error to different utterances spoken by the samespeaker. Since the proposed criteria reduce the variation ofspeaker characteristics caused by changes in background envi-ronment or spoken content, the resulting embeddings of eachspeaker become more consistent. The effectiveness of the pro-posed method is demonstrated through two tasks; disentangle-ment performance, and improvement of speaker recognition ac-curacy compared to the baseline model on a benchmark dataset,VoxCeleb1. Ablation studies also show the impact of each cri-terion on overall performance.

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arXiv2020

FaceFilter: Audio-visual speech separation using still images

The objective of this paper is to separate a target speaker's speech from a mixture of two speakers using a deep audio-visual speech separation network. Unlike previous works that used lip movement on video clips or pre-enrolled speaker information as an auxiliary conditional feature, we use a single face image of the target speaker. In this task, the conditional feature is obtained from facial appearance in cross-modal biometric task, where audio and visual identity representations are shared in latent space. Learnt identities from facial images enforce the network to isolate matched speakers and extract the voices from mixed speech. It solves the permutation problem caused by swapped channel outputs, frequently occurred in speech separation tasks. The proposed method is far more practical than video-based speech separation since user profile images are readily available on many platforms. Also, unlike speaker-aware separation methods, it is applicable on separation with unseen speakers who have never been enrolled before. We show strong qualitative and quantitative results on challenging real-world examples.

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arXiv2020

Seeing voices and hearing voices: learning discriminative embeddings using cross-modal self-supervision

The goal of this work is to train discriminative cross-modal embeddings without access to manually annotated data. Recent advances in self-supervised learning have shown that effective representations can be learnt from natural cross-modal synchrony. We build on earlier work to train embeddings that are more discriminative for uni-modal downstream tasks. To this end, we propose a novel training strategy that not only optimises metrics across modalities, but also enforces intra-class feature separation within each of the modalities. The effectiveness of the method is demonstrated on two downstream tasks: lip reading using the features trained on audio-visual synchronisation, and speaker recognition using the features trained for cross-modal biometric matching. The proposed method outperforms state-of-the-art self-supervised baselines by a signficant margin.

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arXiv2019

Orthonormal Embedding-based Deep Clustering for Single-channel Speech Separation

Deep clustering is a deep neural network-based speech separation algorithm that first trains the mixed component of signals with high-dimensional embeddings, and then uses a clustering algorithm to separate each mixture of sources. In this paper, we extend the baseline criterion of deep clustering with an additional regularization term to further improve the overall performance. This term plays a role in assigning a condition to the embeddings such that it gives less correlation to each embedding dimension, leading to better decomposition of the spectral bins. The regularization term helps to mitigate the unavoidable permutation problem in the conventional deep clustering method, which enables to bring better clustering through the formation of optimal embeddings. We evaluate the results by varying embedding dimension, signal-to-interference ratio (SIR), and gender dependency. The performance comparison with the source separation measurement metric, i.e. signal-to-distortion ratio (SDR), confirms that the proposed method outperforms the conventional deep clustering method.

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arXiv2018

Perfect match: Improved cross-modal embeddings for audio-visual synchronisation

This paper proposes a new strategy for learning powerful cross-modal embeddings for audio-to-video synchronization. Here, we set up the problem as one of cross-modal retrieval, where the objective is to find the most relevant audio segment given a short video clip. The method builds on the recent advances in learning representations from cross-modal self-supervision. The main contributions of this paper are as follows: (1) we propose a new learning strategy where the embeddings are learnt via a multi-way matching problem, as opposed to a binary classification (matching or non-matching) problem as proposed by recent papers; (2) we demonstrate that performance of this method far exceeds the existing baselines on the synchronization task; (3) we use the learnt embeddings for visual speech recognition in self-supervision, and show that the performance matches the representations learnt end-to-end in a fully-supervised manner.

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