In this paper, an improved strategy for automated text dependent speaker identification system has been proposed in noisy environment. The identification process incorporates the Neuro- Genetic hybrid algorithm with cepstral based features. To remove the background noise from the source utterance, wiener filter has been used. Different speech pre-processing techniques such as start-end point detection algorithm, pre-emphasis filtering, frame blocking and windowing have been used to process the speech utterances. RCC, MFCC, MFCC, MFCC, LPC and LPCC have been used to extract the features. After feature extraction of the speech, Neuro-Genetic hybrid algorithm has been used in the learning and identification purposes. Features are extracted by using different techniques to optimize the performance of the identification. According to the VALID speech database, the highest speaker identification rate of 100.000 percent for studio environment and 82.33 percent for office environmental conditions have been achieved in the close set text dependent speaker identification system.
Improved Feature Extraction Network for Neuro-Oriented Target Speaker Extraction
School of Computer Science and Technology, Anhui University（安徽大学计算机科学与技术学院）；National University of Singapore（新加坡国立大学）；University of Science and Technology of China (USTC)（中国科学技术大学）
EEG-informed attended speaker extraction from recorded speech mixtures with application in neuro-steered hearing prostheses
ESAT Laboratory of KU Leuven（KU莱顿大学ESAT实验室）；KU Leuven（KU莱顿大学）；Department of Electrical Engineering (ESAT)（电气工程系（ESAT））；Department of Neurosciences（神经科学系）
Parkinson's disease is a neurodegenerative disorder characterized by the presence of different motor impairments. Information from speech, handwriting, and gait signals have been considered to evaluate the neurological state of the patients. On the other hand, user models based on Gaussian mixture models - universal background models (GMM-UBM) and i-vectors are considered the state-of-the-art in biometric applications like speaker verification because they are able to model specific speaker traits. This study introduces the use of GMM-UBM and i-vectors to evaluate the neurological state of Par…
Human brain performs remarkably well in segregating a particular speaker from interfering ones in a multi-speaker scenario. It has been recently shown that we can quantitatively evaluate the segregation capability by modelling the relationship between the speech signals present in an auditory scene and the cortical signals of the listener measured using electroencephalography (EEG). This has opened up avenues to integrate neuro-feedback into hearing aids whereby the device can infer user's attention and enhance the attended speaker. Commonly used algorithms to infer the auditory attention are …
1 College of Electronic；Optical Engineering \& College of Flexible Electronics (Future Technology), Nanjing University of Posts；Telecommunications, Nanjing, China 2 School of Artificial Intelligence, School of Data Science, SRIBD, The Chinese University of Hong Kong, Shenzhen, China
Identifying Speaker Information in Feed-Forward Layers of Self-Supervised Speech Transformers
Graduate Institute of Communication Engineering, National Taiwan University（国立台湾大学通信工程研究所）；Centre of Speech Technology Research, University of Edinburgh（爱丁堡大学语音技术研究中心）
University of Coimbra（科英布拉大学）；Institute of Systems and Robotics（系统与机器人研究所）
Human beings have developed fantastic abilities to integrate information from various sensory sources exploring their inherent complementarity. Perceptual capabilities are therefore heightened, enabling, for instance, the well-known "cocktail party" and McGurk effects, i.e., speech disambiguation from a panoply of sound signals. This fusion ability is also key in refining the perception of sound source location, as in distinguishing whose voice is being heard in a group conversation. Furthermore, neuroscience has successfully identified the superior colliculus region in the brain as the one re…
Breaking Shortcut Learning for Cross-Trial EEG-Guided Target Speech Extraction via Two-Stage Training
Department of Intelligence and Information（智能信息系）；Seoul National University（首尔国立大学）；Department of Communication Sciences and Disorders（沟通科学与障碍系）；University of Iowa（爱荷华大学）；Interdisciplinary Program in Artificial Intelligence（人工智能交叉学科项目）；Artificial Intelligence Institute（人工智能研究所）
Structured Speaker-Deficiency Adaptation of Foundation Models for Dysarthric and Elderly Speech Recognition
The Chinese University of Hong Kong（香港中文大学）；Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；National Research Council of Canada（加拿大国家研究委员会）
CUNY Graduate Center（纽约市立大学研究生中心）；York College (CUNY)（纽约市立大学约克学院）；Brooklyn College (CUNY)（纽约市立大学布鲁克林学院）
Personality have been found to predict many life outcomes, and there have been huge interests on automatic personality recognition from a speaker's utterance. Previously, we achieved accuracies between 37%-44% for three-way classification of high, medium or low for each of the Big Five personality traits (Openness to Experience, Conscientiousness, Extraversion, Agreeableness, Neuroticism). We show here that we can improve performance on this task by accounting for heterogeneity of gender and L1 in our data, which has English speech from female and male native speakers of Chinese and Standard A…
University of Augsburg（奥格斯堡大学）；Imperial College London（帝国理工学院）
N-HANS is a Python toolkit for in-the-wild audio enhancement, including speech, music, and general audio denoising, separation, and selective noise or source suppression. The functionalities are realised based on two neural network models sharing the same architecture, but trained separately. The models are comprised of stacks of residual blocks, each conditioned on additional speech or environmental noise recordings for adapting to different unseen speakers or environments in real life. In addition to a Python API, a command line interface is provided to researchers and developers, both of wh…
Egocentric Speaker Classification in Child-Adult Dyadic Interactions: From Sensing to Computational Modeling
University of Southern California（南加州大学）；University of California, San Francisco（加州大学旧金山分校）；Signal Analysis and Interpretation Laboratory（信号分析与解释实验室）；Weill Institute for Neurosciences（魏尔神经科学研究所）
Ecole des Hautes Etudes en Sciences Sociales（巴黎高等社会科学研究院）；Centre National de la Recherche Scientifique（法国国家科学研究中心）；INRIA（法国国家信息与自动化研究所）；Ecole Normale Supérieure-Paris Sciences & Lettres University（巴黎文理研究大学高等师范学院）；Ecole Normale Supérieure（巴黎高等师范学院）；Institut national de la santé et de la recherche médicale（法国国家健康与医学研究院）；Institut Mondor de Recherche Biomédicale（蒙多生物医学研究所）；Neuratris（Neuratris研究所）；Université Paris-Est Créteil（巴黎东克雷泰伊大学）；Paris Sciences & Lettres University（巴黎文理研究大学）；Facebook AI Research（脸书人工智能研究院）
Deep Learning models have become potential candidates for auditory neuroscience research, thanks to their recent successes on a variety of auditory tasks. Yet, these models often lack interpretability to fully understand the exact computations that have been performed. Here, we proposed a parametrized neural network layer, that computes specific spectro-temporal modulations based on Gabor kernels (Learnable STRFs) and that is fully interpretable. We evaluated predictive capabilities of this layer on Speech Activity Detection, Speaker Verification, Urban Sound Classification and Zebra Finch Cal…
The Chinese University of Hong Kong（香港中文大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
Disordered speech recognition is a highly challenging task. The underlying neuro-motor conditions of people with speech disorders, often compounded with co-occurring physical disabilities, lead to the difficulty in collecting large quantities of speech required for system development. This paper investigates a set of data augmentation techniques for disordered speech recognition, including vocal tract length perturbation (VTLP), tempo perturbation and speed perturbation. Both normal and disordered speech were exploited in the augmentation process. Variability among impaired speakers in both th…
The Chinese University of Hong Kong（香港中文大学）；Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）
Automatic recognition of disordered speech remains a highly challenging task to date. The underlying neuro-motor conditions, often compounded with co-occurring physical disabilities, lead to the difficulty in collecting large quantities of impaired speech required for ASR system development. This paper presents novel variational auto-encoder generative adversarial network (VAE-GAN) based personalized disordered speech augmentation approaches that simultaneously learn to encode, generate and discriminate synthesized impaired speech. Separate latent features are derived to learn dysarthric speec…
Encoder-Side Neuron Identification and Amplification for Acoustic Perception in Large Audio-Language Models
National Taiwan University（国立台湾大学）；Graduate Institute of Communication Engineering（通信工程研究所）；NTU Artificial Intelligence Center of Research Excellence（国立交通大学人工智能卓越研究中心）；ASUS Open Cloud Infrastructure Software Center（ASUS开放云基础设施软件中心）
Spiking Neural Networks (SNN) are known to be very effective for neuromorphic processor implementations, achieving orders of magnitude improvements in energy efficiency and computational latency over traditional deep learning approaches. Comparable algorithmic performance was recently made possible as well with the adaptation of supervised training algorithms to the context of SNN. However, information including audio, video, and other sensor-derived data are typically encoded as real-valued signals that are not well-suited to SNN, preventing the network from leveraging spike timing informatio…
Confidence-Based Self-Training for EMG-to-Speech: Leveraging Synthetic EMG for Robust Modeling
ETIS, CY Cergy Paris Université - ENSEA – CNRS, UMR 8051（ETIS，CY 塞尔吉巴黎大学 - ENSEA – CNRS，UMR 8051）；Institute for Infocomm Research, A STAR（信息与通信研究所以院）；IPAL (International Research Laboratory on Artificial Intelligence), CNRS（人工智能国际研究实验室，CNRS）
Conventional feature-based classification methods do not apply well to automatic recognition of speech emotions, mostly because the precise set of spectral and prosodic features that is required to identify the emotional state of a speaker has not been determined yet. This paper presents a method that operates directly on the speech signal, thus avoiding the problematic step of feature extraction. Furthermore, this method combines the strengths of the classical source-filter model of human speech production with those of the recently introduced liquid state machine (LSM), a biologically-inspir…
As a neurophysiological response to threat or adverse conditions, stress can affect cognition, emotion and behaviour with potentially detrimental effects on health in the case of sustained exposure. Since the affective content of speech is inherently modulated by an individual's physical and mental state, a substantial body of research has been devoted to the study of paralinguistic correlates of stress-inducing task load. Historically, voice stress analysis (VSA) has been conducted using conventional digital signal processing (DSP) techniques. Despite the development of modern methods based o…
