Learning speaker recognition models through human-robot interaction

Martinson, Eric; Lawson, Wallace

doi:10.1109/icra.2011.5980243

Cited by 4 publications

(1 citation statement)

References 11 publications

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“…In (Ji et al, 2007) the "Wever" robot recognizes the speaker through multiple microphones to offer a natural and familiar interface. Martinson and Lawson (2011) proposed a multimodal audio-visual recognition system on board of the Octavia robot to effectively track the speaker in partylike conversation (i.e. multiple active speakers).…”

Section: Introductionmentioning

confidence: 99%

Few-shot re-identification of the speaker by social robots

et al. 2022

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Nowadays advanced machine learning, computer vision, audio analysis and natural language understanding systems can be widely used for improving the perceptive and reasoning capabilities of the social robots. In particular, artificial intelligence algorithms for speaker re-identification make the robot aware of its interlocutor and able to personalize the conversation according to the information gathered in real-time and in the past interactions with the speaker. Anyway, this kind of application requires to train neural networks having available only a few samples for each speaker. Within this context, in this paper we propose a social robot equipped with a microphone sensor and a smart deep learning algorithm for few-shot speaker re-identification, able to run in real time over an embedded platform mounted on board of the robot. The proposed system has been experimentally evaluated over the VoxCeleb1 dataset, demonstrating a remarkable re-identification accuracy by varying the number of samples per speaker, the number of known speakers and the duration of the samples, and over the SpReW dataset, showing its robustness in real noisy environments. Finally, a quantitative evaluation of the processing time over the embedded platform proves that the processing pipeline is almost immediate, resulting in a pleasant user experience.

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Section: Introductionmentioning

confidence: 99%

Few-shot re-identification of the speaker by social robots

et al. 2022

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Multi-task learning for X-vector based speaker recognition

Zhang,

Liu

2023

Int J Speech Technol

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In this paper, we propose a speaker recognition system that leverages multi-task learning and features integration (MTFI), to improve the performance of x-vector based speaker recognition models. It is important to integrate complementary information from different features such as MFCC, Fbank, spectrogram and LPCC, as often a single feature usually cannot cover all information about a speaker and generalization is insufficient. Since the x-vector model outputs affine transformation values with the penultimate hidden layer in the trained model, the parameter distribution of this layer should be stable and should not be affected by tasks that are not current branches when switching tasks. Therefore, we propose a shared unit (SU) in multi-task learning, which is useful for sharing common representations and other auxiliary tasks. Then, an attention mechanism is designed to calculate the frame weight in the statistical pooling layer, so as to enhance the key frame information. The proposed system had an EER of 0.98% in voxceleb1 and the average score fusion obtained the EER of 0.65%.

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Speaker Recognition Based on Long-Term Acoustic Features With Analysis Sparse Representation

Lin

Zhang

2019

IEEE Access

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The performance of a speaker recognition system depends highly on which acoustic features are used. Most speaker recognition systems use short-term acoustic features extracted from a single speech frame, and the most popular short-term acoustic features are the Mel-frequency cepstral coefficients (MFCCs). The short-term features are generally static features no dynamic information in the speech signal is included in either cepstral coefficients or an MFCCs frame. Using an analysis sparse representation model, in this paper, we introduce the long-term acoustic (LTA) feature for text-independent speaker recognition, which is a sparse presentation of the static features and dynamic information for the speaker's speech. First, the speech signal is segmented into frames which are overlapping with each other, and then the MFCCs frame features can be extracted to construct some super MFCCs frames by stacking some following frames of the current frame to capture the dynamic information of the speech signal. The super MFCCs frames can be combined into a 2-D MFCCs features map (MFCCsmap). Finally, the speaker model can be built based on the analysis sparse model and the sparse representations of the MFCCsmap are used as the LTA features. A state-of-the-art deep neural network (DNN) is employed as a classifier for speaker recognition. The experimental results illustrate the effectiveness and robustness of the proposed system.INDEX TERMS Analysis sparse representation, deep neural network, long-term acoustic features, Mel-frequency cepstral coefficients, speaker recognition.

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Learning speaker recognition models through human-robot interaction

Cited by 4 publications

References 11 publications

Few-shot re-identification of the speaker by social robots

Few-shot re-identification of the speaker by social robots

Multi-task learning for X-vector based speaker recognition

Speaker Recognition Based on Long-Term Acoustic Features With Analysis Sparse Representation

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