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Speaker identification, a cornerstone of speech processing, involves associating individuals with spoken segments within a known speaker pool. This paper presents a significant AI contribution: an innovative framework tailored for closed-set ...

• Short-duration speech segment focus improves model’s practical applicability.
• Architectural changes increase neural networks’ vocal pattern precision.
• Integration of Wav2Vec2 framework advances state-of-the-art in SID.
• Novel ...

In order to process speech, most state-of-the-art experimental methods employ convolutional neural networks (CNNs), which operate on a continuous, 1-dimensional (1-D) time stream. In an audio signal, the mel-spectrogram facilitates the ...

With the widespread application of speaker identification (SI) systems in security-related tasks, the robustness of SI systems against adversarial examples has garnered increasing attention. Existing works have demonstrated the vulnerability of ...

Voice is perceived as a form of biometrics which communicates valuable and rich information pertinent to an individual, such as his or her identity, gender, accent, age and emotion. Speaker identification denotes the task of identifying speakers ...

Short-utterance speaker identification is a difficult area of study in natural language processing (NLP). Most cutting-edge experimental approaches for speech processing make use of convolutional neural networks (CNNs) and deep neural networks and ...

This paper presents the newly developed non-native children’s English speech (NNCES) corpus to reveal the findings of automatic speaker and age recognition from raw speech. Convolutional neural networks (CNN), which have the ability to learn low-...

• The study proposes the use of the SincNet model to extract significant speech cues from children’s raw speech, and evaluates its effectiveness for automatic speaker and age identification tasks.
• The article highlights the benefits of ...

Advancements in deep learning for speaker identification are constrained by the limited availability of data, especially in law enforcement applications. This has led to the emergence of few-shot speaker identification, a technique that ...

Machine learning provides researchers in speech processing and bioacoustics numerous advanced and non-invasive techniques to investigate animal vocalizations. Hidden Markov Models (HMMs) are machine learning techniques that were developed and ...

The human voice, a dynamic signal, conveys valuable information for speaker identification, encompassing gender, age, emotions, and language. In the biometrics industry, identifying voices in real-time amidst diverse accents, tones, and noisy ...

Advancements in machine learning and deep learning benefit access control systems, forensics, and biometrics particularly in speaker identification systems. The SincNet architecture is a distinct convolutional neural network (CNN) designed for ...

This paper conducts comparative analysis of speaker identification techniques, namely Gaussian Mixture Models (GMM), Bidirectional Long Short-Term Memory (BiLSTM) neural networks, and Recurrent Neural Networks (RNN), applied to the Kazakh Speech ...

Speaker identification based on deep learning is known to be susceptible to backdoor attacks. However, the current research on audio backdoor attacks is limited, and these attacks often use obvious noises as triggers, which can raise suspicion ...

• Backdoor attack against speaker identification, improving the imperceptibility.
• Optimized steganographic network for embedding triggers secretively.
• Two-stage adversarial optimization to get indistinguishable malicious samples.

Speech-based speaker identification system is one of the alternatives to the conventional biometric contact-based identification systems. Recent works demonstrate the growing interest among researchers in this field and highlight the practical ...

Hidden Markov models (HMMs) were developed and implemented to discriminate between each of the 2 ages, 11 call-types, and 51 speakers of birds using cross-validation on the recordings in the 3314 database for chick (19–25 days of age) and adult (...

Speech signals are more susceptible to emotional influences and acoustic interference than other communications. Applications for real-time speech processing face difficulties when dealing with noisy, emotion-filled speech data. ...

• A novel method is proposed to enhance speaker identification in abnormal conditions.

Deep learning has improved the performance of speaker identification systems in recent years, but it has also presented significant challenges. Typically, data-driven modeling approaches based on DNNs rely on large-scale training data, but due to ...

Current state-of-the-art audio analysis algorithms based on deep learning rely on hand-crafted Spectrogram-like audio representations, that are more compact than descriptors obtained from the raw waveform; the latter are, in turn, far from ...

Speaker identification aims at determining the speaker identity by analyzing his voice characteristics, and relies typically on statistical models or machine learning techniques. Frequency-domain features are by far the most used ...

• We present a late fusion DNN model with RWs and GTCCs for speaker identification.

Deep learning has provided many advantages including the ability to extract features from the voice samples and represents the data in a more decisive mode. In speaker identification, many studies have been done to extract more and more meaningful ...

The use of machine learning in automatic speaker identification and localization systems has recently seen significant advances. However, this progress comes at the cost of using complex models, computations, and increasing the number of ...