Lightweight speaker verification for online identification of new speakers with short segments

Vélez, Ivette; Rascón, Caleb; Fuentes-Pineda, Gibrán

doi:10.1016/j.asoc.2020.106704

Cited by 8 publications

(2 citation statements)

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“…The results show that BGCC-PWPE feature and Triplet-DAM with coupled architecture maintains a large performance advantage. At the same time, the experiments comparing with the current best performing techniques of the text-independent speaker recognition and short duration voiceprint recognition like the GE2E Loss [46], 1D-Triplet-CNN-MFCC-LPC [4], and ResNet-34 + SpecdB [47], from the performances on VoxCeleb-2 and NIST SRE 2010 dataset in Table 6, we can see that, with the proposed method of Trip-DAM-model based on BGCC-PWPE acoustic features, the system performance is further improved in the 3-second speech length and full-length speech. scaled Gauss filter method and effectiveness of perceptual wavelet packet entropy with non-stationary speech feature extraction, which is better at extracting reliable voiceprint and high-frequency features of the speaker and learns a high-resolution embedding of information that improves short-duration speaker recognition performance, concurrently, compensating for the weaknesses of discriminable feature sparsity.…”

Section: Resultsmentioning

confidence: 99%

“…In such circumstances, conventional speaker models based on statistical characteristics fall short of accurately describing speakers. Nevertheless, short duration audio signals have always been momentous difficulties of speaker recognition [6,7]. Simultaneously, acoustic feature extraction is a critical component of short duration speech speaker recognition system [8].…”

Section: Introductionmentioning

confidence: 99%

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Improving short-duration speaker recognition by joint Bark-wavelet acoustic feature coupling and triplet dual-attention mechanism network

Xiong

2023

Preprint

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Speaker recognition methods are negatively affected by the input short duration audio signal. In this paper, we tackle the problem of speaker recognition from shorter speech data by coupling proposed two acoustic features: Bark-scaled Gauss Filter Cepstral Coefficients (BGCC) and Perceptual Wavelet Packet Entropy (PWPE). Our assumption is based on the observation that BGCC and PWPE capture sufficient information on various aspects of speech that can be used to discriminate speaker, viz., speech perception and high time-frequency information representation, etc., for enhancing characteristic diversity. A triplet dual attention mechanism (Triplet-DAM) is used to couple these two features in a creative manner. The coupling method means that the feature of limited short utterance can be reused, through the dual attention mechanism, more discriminative features are enhanced for limited feature, thus improving speaker recognition performance in short duration audio signals. Extensive analysis on a variety of datasets, which speech samples of different types, diverse lengths, etc., demonstrate the superiority of the proposed feature engineering and method over existing acoustic feature extraction and speaker recognition algorithms, including those based on MFCCs, LPCCs features, and GMM-UBM, iVector-PLDA, ResCNN-triplet, respectively. The experimental results demonstrate the proposed method achieves notable improvement with the existing approach for short duration speaker recognition.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improving short-duration speaker recognition by joint Bark-wavelet acoustic feature coupling and triplet dual-attention mechanism network

Xiong

2023

Preprint

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The human in the loop Perspectives and challenges for RoboCup 2050

Rossi,

Paetzel-Prüsmann,

Keijsers

et al. 2024

Auton Robot

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Robotics researchers have been focusing on developing autonomous and human-like intelligent robots that are able to plan, navigate, manipulate objects, and interact with humans in both static and dynamic environments. These capabilities, however, are usually developed for direct interactions with people in controlled environments, and evaluated primarily in terms of human safety. Consequently, human-robot interaction (HRI) in scenarios with no intervention of technical personnel is under-explored. However, in the future, robots will be deployed in unstructured and unsupervised environments where they will be expected to work unsupervised on tasks which require direct interaction with humans and may not necessarily be collaborative. Developing such robots requires comparing the effectiveness and efficiency of similar design approaches and techniques. Yet, issues regarding the reproducibility of results, comparing different approaches between research groups, and creating challenging milestones to measure performance and development over time make this difficult. Here we discuss the international robotics competition called RoboCup as a benchmark for the progress and open challenges in AI and robotics development. The long term goal of RoboCup is developing a robot soccer team that can win against the world’s best human soccer team by 2050. We selected RoboCup because it requires robots to be able to play with and against humans in unstructured environments, such as uneven fields and natural lighting conditions, and it challenges the known accepted dynamics in HRI. Considering the current state of robotics technology, RoboCup’s goal opens up several open research questions to be addressed by roboticists. In this paper, we (a) summarise the current challenges in robotics by using RoboCup development as an evaluation metric, (b) discuss the state-of-the-art approaches to these challenges and how they currently apply to RoboCup, and (c) present a path for future development in the given areas to meet RoboCup’s goal of having robots play soccer against and with humans by 2050.

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Improving Short-Duration Speaker Recognition by Joint Bark-Wavelet Acoustic Feature Coupling and Triplet Dual-Attention Mechanism Network

Zi,

Xiong

2024

Wireless Pers Commun

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Lightweight speaker verification for online identification of new speakers with short segments

Cited by 8 publications

References 10 publications

Improving short-duration speaker recognition by joint Bark-wavelet acoustic feature coupling and triplet dual-attention mechanism network

Improving short-duration speaker recognition by joint Bark-wavelet acoustic feature coupling and triplet dual-attention mechanism network

The human in the loop Perspectives and challenges for RoboCup 2050

Improving Short-Duration Speaker Recognition by Joint Bark-Wavelet Acoustic Feature Coupling and Triplet Dual-Attention Mechanism Network

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