134,413 research outputs found
Speaker recognition under stress conditions
Proceeding of: IberSPEECH 2018, 21-23 November 2018, Barcelona, SpainSpeaker recognition systems exhibit a decrease in performance when the input speech is not in optimal circumstances, for example when the user is under emotional or stress conditions. The objective of this paper is measuring the effects of stress on speech to ultimately try to mitigate its consequences on a speaker recognition task. On this paper, we develop a stress-robust speaker identification system using data selection and augmentation by means of the manipulation of the original speech utterances. An extensive experimentation has been carried out for assessing the effectiveness of the proposed techniques. First, we concluded that the best performance is always obtained when naturally stressed samples are included in the training set, and second, when these are not available, their substitution and augmentation with synthetically generated stress-like samples, improves the performance of the system.This work is partially supported by the Spanish Government-MinECo projects TEC2014-53390-P and TEC2017-84395-P
End-to-end Recurrent Denoising Autoencoder Embeddings for Speaker Identification
Speech 'in-the-wild' is a handicap for speaker recognition systems due to the
variability induced by real-life conditions, such as environmental noise and
emotions in the speaker. Taking advantage of representation learning, on this
paper we aim to design a recurrent denoising autoencoder that extracts robust
speaker embeddings from noisy spectrograms to perform speaker identification.
The end-to-end proposed architecture uses a feedback loop to encode information
regarding the speaker into low-dimensional representations extracted by a
spectrogram denoising autoencoder. We employ data augmentation techniques by
additively corrupting clean speech with real life environmental noise and make
use of a database with real stressed speech. We prove that the joint
optimization of both the denoiser and the speaker identification module
outperforms independent optimization of both modules under stress and noise
distortions as well as hand-crafted features.Comment: 8 pages + 2 of references + 5 of images. Submitted on Monday 20th of
July to Elsevier Signal Processing Short Communication
Data Augmentation for Speaker Identification under Stress Conditions to Combat Gender-Based Violence
This article belongs to the Special Issue IberSPEECH 2018: Speech and Language Technologies for Iberian LanguagesA Speaker Identification system for a personalized wearable device to combat gender-based violence is presented in this paper. Speaker recognition systems exhibit a decrease in performance when the user is under emotional or stress conditions, thus the objective of this paper is to measure the effects of stress in speech to ultimately try to mitigate their consequences on a speaker identification task, by using data augmentation techniques specifically tailored for this purpose given the lack of data resources for this condition. An extensive experimentation has been carried out for assessing the effectiveness of the proposed techniques. First, we conclude that the best performance is always obtained when naturally stressed samples are included in the training set, and second, when these are not available, their substitution and augmentation with synthetically generated stress-like samples improves the performance of the system.This work is partially supported by the Spanish Government-MinECo project TEC2017-84395-P and Madrid Regional Project Y2018/TCS-5046
Speaker Recognition System using Wavelet Transform under Stress Condition
in this paper, we introduced a text-depend speaker recognition by using wavelet transform under stressed conditions. Here we compare different feature such as ARC, LAR, LPCC, MFCC, CEP and after comparison we found that LPCC provides best feature. For decompose signal at two levels Discrete Wavelet Transform is used here. Discrete Wavelet Transform (DWT) based Linear Predictive Cepstral Coefficients (LPCC) used as a feature for recognized the speaker system. For classification Vector Quantization method is used. Four different stressed data has selected for (SUSAS) i.e. stress speech data base for speaker recognition. Improvement is achieved 93% and 94% in case of Lombard and Neutral case
Employing Emotion Cues to Verify Speakers in Emotional Talking Environments
Usually, people talk neutrally in environments where there are no abnormal
talking conditions such as stress and emotion. Other emotional conditions that
might affect people talking tone like happiness, anger, and sadness. Such
emotions are directly affected by the patient health status. In neutral talking
environments, speakers can be easily verified, however, in emotional talking
environments, speakers cannot be easily verified as in neutral talking ones.
Consequently, speaker verification systems do not perform well in emotional
talking environments as they do in neutral talking environments. In this work,
a two-stage approach has been employed and evaluated to improve speaker
verification performance in emotional talking environments. This approach
employs speaker emotion cues (text-independent and emotion-dependent speaker
verification problem) based on both Hidden Markov Models (HMMs) and
Suprasegmental Hidden Markov Models (SPHMMs) as classifiers. The approach is
comprised of two cascaded stages that combines and integrates emotion
recognizer and speaker recognizer into one recognizer. The architecture has
been tested on two different and separate emotional speech databases: our
collected database and Emotional Prosody Speech and Transcripts database. The
results of this work show that the proposed approach gives promising results
with a significant improvement over previous studies and other approaches such
as emotion-independent speaker verification approach and emotion-dependent
speaker verification approach based completely on HMMs.Comment: Journal of Intelligent Systems, Special Issue on Intelligent
Healthcare Systems, De Gruyter, 201
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