Phonetically-Aware Coupled Network For Short Duration Text-Independent Speaker Verification

Zheng, Siqi; Lei, Yun; Suo, Hongbin

doi:10.21437/interspeech.2020-1306

Cited by 8 publications

(9 citation statements)

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“…TDNN, also known as dilated one-dimensional convolution (Dilated 1D Conv), has been widely used in speaker verification tasks [1,4,5,6]. In [1], researchers propose a TDNN-based model for extracting speaker embeddings.…”

Section: Speaker Embeddingmentioning

confidence: 99%

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Cam: Context-Aware Masking for Robust Speaker Verification

Zheng

Suo

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Performance degradation caused by noise has been a long-standing challenge for speaker verification. Previous methods usually involve applying a denoising transformation to speaker embeddings or enhancing input features. Nevertheless, these methods are lossy and inefficient for speaker embedding. In this paper, we propose contextaware masking (CAM), a novel method to extract robust speaker embedding. CAM enables the speaker embedding network to "focus" on the speaker of interest and "blur" unrelated noise. The threshold of masking is dynamically controlled by an auxiliary context embedding that captures speaker and noise characteristics. Moreover, models adopting CAM can be trained in an end-to-end manner without using synthesized noisy-clean speech pairs. Our results show that CAM improves speaker verification performance in the wild by a large margin, compared to the baselines.

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Section: Speaker Embeddingmentioning

confidence: 99%

“…In the past few years, deep learning-based speaker embedding has made significant progress. Speaker verification systems utilizing deep learning-based speaker embedding have shown state-of-the-art performance [1,2,3,4,5,6].…”

Section: Introductionmentioning

confidence: 99%

Cam: Context-Aware Masking for Robust Speaker Verification

Zheng

Suo

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…Phonetically-aware coupled network(PacNet) as described in [16] is implemented. PacNet focuses on normalizing the effects of phonetic contents by directly comparing both the phonetic and acoustic information of the two utterances.…”

Section: Phonetically-aware Coupled Networkmentioning

confidence: 99%

A Real-Time Speaker Diarization System Based on Spatial Spectrum

Zheng

Huang

Wang

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In this paper we describe a speaker diarization system that enables localization and identification of all speakers present in a conversation or meeting. We propose a novel systematic approach to tackle several long-standing challenges in speaker diarization tasks: (1) to segment and separate overlapping speech from two speakers;(2) to estimate the number of speakers when participants may enter or leave the conversation at any time; (3) to provide accurate speaker identification on short text-independent utterances; (4) to track down speakers movement during the conversation; (5) to detect speaker change incidence real-time. First, a differential directional microphone array-based approach is exploited to capture the target speakers' voice in far-field adverse environment. Second, an online speaker-location joint clustering approach is proposed to keep track of speaker location. Third, an instant speaker number detector is developed to trigger the mechanism that separates overlapped speech. The results suggest that our system effectively incorporates spatial information and achieves significant gains.

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“…In [17], phonetic features are projected into multi-channel feature maps. Along similar direction, PacNet [18] uses coupled stem to jointly learn acoustic features and transform framelevel ASR bottleneck feature. In [25], a speaker-utterance dual attention (SUDA) is applied to learn the interaction between speaker and utterance information streams in a unified framework for text-dependent SV.…”

Section: Introductionmentioning

confidence: 99%

Neural Acoustic-Phonetic Approach for Speaker Verification With Phonetic Attention Mask

Liu

Das²,

Lee

et al. 2022

IEEE Signal Process. Lett.

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Traditional acoustic-phonetic approach makes use of both spectral and phonetic information when comparing the voice of speakers. While phonetic units are not equally informative, the phonetic context of speech plays an important role in speaker verification (SV). In this paper, we propose a neural acousticphonetic approach that learns to dynamically assign differentiated weights to spectral features for SV. Such differentiated weights form a phonetic attention mask (PAM). The neural acoustic-phonetic framework consists of two training pipelines, one for SV and another for speech recognition. Through the PAM, we leverage the phonetic information for SV. We evaluate the proposed neural acoustic-phonetic framework on the RSR2015 database Part III corpus, that consists of random digit strings. We show that the proposed framework with PAM consistently outperforms baseline with an equal error rate reduction of 13.45% and 10.20% for female and male data, respectively.

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Phonetically-Aware Coupled Network For Short Duration Text-Independent Speaker Verification

Cited by 8 publications

References 0 publications

Cam: Context-Aware Masking for Robust Speaker Verification

Cam: Context-Aware Masking for Robust Speaker Verification

A Real-Time Speaker Diarization System Based on Spatial Spectrum

Neural Acoustic-Phonetic Approach for Speaker Verification With Phonetic Attention Mask

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