i-vector based speaker recognition on short utterances

Kanagasundaram, Ahilan; Vogt, Robbie; Dean, David; Sridharan, Sridha; Mason, Michael

doi:10.21437/interspeech.2011-58

Cited by 171 publications

(46 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, in [5], the authors showed that the performance on short utterances can be improved by JFA. This work was extended in [6] which reported that the i-vector model can distill speaker information in a more effective way so it is more suitable for SUSR. In addition, a score-based segment selection technique was proposed in [7].…”

Section: Introductionmentioning

confidence: 98%

System combination for short utterance speaker recognition

Wang

Zhang

et al. 2016

2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

View full text Add to dashboard Cite

For text-independent short-utterance speaker recognition (SUSR), the performance often degrades dramatically. This paper presents a combination approach to the SUSR tasks with two phonetic-aware systems: one is the DNN-based ivector system and the other is our recently proposed subregionbased GMM-UBM system. The former employs phone posteriors to construct an i-vector model in which the shared statistics offers stronger robustness against limited test data, while the latter establishes a phone-dependent GMM-UBM system which represents speaker characteristics with more details. A score-level fusion is implemented to integrate the respective advantages from the two systems. Experimental results show that for the textindependent SUSR task, both the DNN-based i-vector system and the subregion-based GMM-UBM system outperform their respective baselines, and the score-level system combination delivers performance improvement.

show abstract

Section: Introductionmentioning

confidence: 98%

System combination for short utterance speaker recognition

Wang

Zhang

et al. 2016

2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

View full text Add to dashboard Cite

show abstract

“…Since i-vectors contain both speaker and channel information, they fail to provide speaker embeddings robust to the nuisance factors [3], requiring additional supervised compensation steps [4,5]. With the advances in deep learning technologies, robust speaker modeling approaches based on neural networks have been proposed [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Robust Speaker Recognition Using Unsupervised Adversarial Invariance

Peri

Pal

Jati

et al. 2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

View full text Add to dashboard Cite

In this paper, we address the problem of speaker recognition in challenging acoustic conditions using a novel method to extract robust speaker-discriminative speech representations. We adopt a recently proposed unsupervised adversarial invariance architecture to train a network that maps speaker embeddings extracted using a pretrained model onto two lower dimensional embedding spaces. The embedding spaces are learnt to disentangle speaker-discriminative information from all other information present in the audio recordings, without supervision about the acoustic conditions. We analyze the robustness of the proposed embeddings to various sources of variability present in the signal for speaker verification and unsupervised clustering tasks on a large-scale speaker recognition corpus. Our analyses show that the proposed system substantially outperforms the baseline in a variety of challenging acoustic scenarios. Furthermore, for the task of speaker diarization on a real-world meeting corpus, our system shows a relative improvement of 36% in the diarization error rate compared to the state-of-the-art baseline.

show abstract

“…However, if the input length is not long enough, their performances are still degraded. Accordingly, many short-duration SV studies are being conducted to have high performance even with a short utterance [18,19,20].…”

Section: Introductionmentioning

confidence: 99%

Multi-Task Network for Noise-Robust Keyword Spotting and Speaker Verification Using CTC-Based Soft VAD and Global Query Attention

Jung

Goo

et al. 2020

Interspeech 2020

View full text Add to dashboard Cite

Keyword spotting (KWS) and speaker verification (SV) have been studied independently although it is known that acoustic and speaker domains are complementary. In this paper, we propose a multi-task network that performs KWS and SV simultaneously to fully utilize the interrelated domain information. The multi-task network tightly combines sub-networks aiming at performance improvement in challenging conditions such as noisy environments, open-vocabulary KWS, and short-duration SV, by introducing novel techniques of connectionist temporal classification (CTC)-based soft voice activity detection (VAD) and global query attention. Frame-level acoustic and speaker information is integrated with phonetically originated weights so that forms a word-level global representation. Then it is used for the aggregation of feature vectors to generate discriminative embeddings. Our proposed approach shows 4.06% and 26.71% relative improvements in equal error rate (EER) compared to the baselines for both tasks. We also present a visualization example and results of ablation experiments.

show abstract

i-vector based speaker recognition on short utterances

Cited by 171 publications

References 4 publications

System combination for short utterance speaker recognition

System combination for short utterance speaker recognition

Robust Speaker Recognition Using Unsupervised Adversarial Invariance

Multi-Task Network for Noise-Robust Keyword Spotting and Speaker Verification Using CTC-Based Soft VAD and Global Query Attention

Contact Info

Product

Resources

About