Xugang Lu

National Institute of Information and Communications Technology

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Yu Tsao

Research Center for Information Technology Innovation, Academia Sinica

Hisashi Kawai

National Institute of Information and Communications Technology

Peng Shen

National Institute of Information and Communications Technology

Masashi Unoki

Japan Advanced Institute of Science and Technology

Jianwu Dang

Tianjin University

Jianguo Wei

Tianjin University

Shigeki Matsuda

Shiga University

Sheng Li

University of Malaya

Masato Akagi

Japan Advanced Institute of Science and Technology

Szu‐Wei Fu

Nvidia (United Kingdom)

Cooperative Institutions

National Institute of Information and Communications Technology

Research Center for Information Technology Innovation, Academia Sinica

Tianjin University

National Taiwan University

Japan Advanced Institute of Science and Technology

Academia Sinica

Chinese Academy of Sciences

Kyoto University

Doshisha University

National Yang Ming Chiao Tung University

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Local fisher discriminant analysis for spoken language identification

Peng Shen Xugang Lu Lemao Liu Hisashi Kawai

I-vector is a state-of-the-art technique widely used in spoken language identification systems. Since i-vectors include total variability factors, discriminant analysis methods have been introduced to find the most discriminative features while removing the undesired variables for language identification, for example, linear discriminant analysis (LDA) and nonparametric discriminant analysis (NDA). However, these methods either do not consider or use weak local structures of the data. In this study, we introduce a local Fisher discriminant analysis (LFDA) as a post-processing discriminant analysis method to extract the discriminative features from i-vectors. LFDA is a full-rank method which takes the local structure of the data into account for non-Gaussian distribution data, i.e., multimodal. Compared with LDA and NDA, LFDA is a pair-wise local method which enhances the centralization of the distribution of samples in the same class to obtain larger amounts of discriminative features. Experimental results indicate that LFDA is more effective than LDA and NDA for the i-vector-based language identification task.

Discriminative model

Rank (graph theory)

10.1109/icassp.2016.7472794

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Citations (15)

Regularization in a reproducing kernel hubert space for robust voice activity detection

Xugang Lu Masashi Unoki Ryosuke Isotani Hisashi Kawai Satoshi Nakamura

Voice activity detection (VAD) is still a difficult task in noisy environments since the statistical distributions of speech and non-speech features are heavily overlapped in noisy environments. Considering that speech is a special type of acoustic signal that only occupies a small fraction of the whole acoustic space, we have proposed a new speech processing method for VAD by giving constraints on the processing space as a reproducing kernel Hilbert space (RKHS). In the RKHS, the estimation of the speech was regarded as a functional approximation problem. Via a regularization in the RKHS framework, a target function is learned to approximate the speech signal while the noise component is supposed to be smoothed out. In this framework, we could incorporate the nonlinear mapping functions in the approximation implicitly via a kernel function. The approximation function could capture the nonlinear and high-order statistical structure of the speech. Our VAD algorithm is designed on the basis of the power energy in this regularized RKHS. We have tested its performance on CENSREC-1-C data corpus for VAD task. In this paper, we quantified its performance on the discriminability for speech and non-speech, and further compared its performance with several classical VAD algorithms. Experimental results showed that the proposed processing for speech enhanced the discriminability between the distributions of speech and non-speech, and got better performance on the VAD task than the classical VAD algorithms.

Regularization

Kernel (algebra)

10.1109/icosp.2010.5656060

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Citations (2)

TMS: Temporal multi-scale in time-delay neural network for speaker verification

Applied Intelligence (2023)

Ruiteng Zhang Jianguo Wei Xugang Lu Wenhuan Lu Di Jin

Discriminative model

10.1007/s10489-023-04953-2

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Citations (3)

Unified denoising and dereverberation method used in restoration of MTF-based power envelope

IEICE Technical Report; IEICE Tech. Rep. (2012)

Masashi Unoki Shota Morita Xugang Lu

Envelope (radar)

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Incorporating Broad Phonetic Information for Speech Enhancement

Interspeech 2022 (2020)

Yen-Ju Lu Chien-Feng Liao Xugang Lu Jeih-weih Hung Yu Tsao

In noisy conditions, knowing speech contents facilitates listeners to more effectively suppress background noise components and to retrieve pure speech signals.Previous studies have also confirmed the benefits of incorporating phonetic information in a speech enhancement (SE) system to achieve better denoising performance.To obtain the phonetic information, we usually prepare a phoneme-based acoustic model, which is trained using speech waveforms and phoneme labels.Despite performing well in normal noisy conditions, when operating in very noisy conditions, however, the recognized phonemes may be erroneous and thus misguide the SE process.To overcome the limitation, this study proposes to incorporate the broad phonetic class (BPC) information into the SE process.We have investigated three criteria to build the BPC, including two knowledgebased criteria: place and manner of articulatory and one datadriven criterion.Moreover, the recognition accuracies of BPCs are much higher than that of phonemes, thus providing more accurate phonetic information to guide the SE process under very noisy conditions.Experimental results demonstrate that the proposed SE with the BPC information framework can achieve notable performance improvements over the baseline system and an SE system using monophonic information in terms of both speech quality intelligibility on the TIMIT dataset.

10.21437/interspeech.2020-1400

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Citations (12)

Investigation of Semi-Supervised Acoustic Model Training Based on the Committee of Heterogeneous Neural Networks

Interspeech 2022 (2016)

Naoyuki Kanda Shoji Harada Xugang Lu Hisashi Kawai

Training set

10.21437/interspeech.2016-72

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Citations (7)

A Novel Temporal Attentive-Pooling based Convolutional Recurrent Architecture for Acoustic Signal Enhancement

arXiv (Cornell University) (2022)

Tassadaq Hussain Wei-Chien Wang Mandar Gogate Kia Dashtipour Yu Tsao

In acoustic signal processing, the target signals usually carry semantic information, which is encoded in a hierarchal structure of short and long-term contexts. However, the background noise distorts these structures in a nonuniform way. The existing deep acoustic signal enhancement (ASE) architectures ignore this kind of local and global effect. To address this problem, we propose to integrate a novel temporal attentive-pooling (TAP) mechanism into a conventional convolutional recurrent neural network, termed as TAP-CRNN. The proposed approach considers both global and local attention for ASE tasks. Specifically, we first utilize a convolutional layer to extract local information of the acoustic signals and then a recurrent neural network (RNN) architecture is used to characterize temporal contextual information. Second, we exploit a novelattention mechanism to contextually process salient regions of the noisy signals. The proposed ASE system is evaluated using a benchmark infant cry dataset and compared with several well-known methods. It is shown that the TAPCRNN can more effectively reduce noise components from infant cry signals in unseen background noises at challenging signal-to-noise levels.

Pooling

Benchmark (surveying)

SIGNAL (programming language)

10.48550/arxiv.2201.09913

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Robust Front End Processing for Speech Recognition in Reverberant Environments: Utilization of Speech Properties

IEICE Technical Report; IEICE Tech. Rep. (2008)

Petric Rico Xugang Lu Masashi Unoki Masato Akagi Ruediger Hoffmann

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