Performance Of Speaker Identification Systems Research Articles

Speaker identification under noisy conditions using hybrid convolutional neural network and gated recurrent unit

<p><span>Speaker identification is biometrics that classifies or identifies a person from other speakers based on speech characteristics. Recently, deep learning models outperformed conventional machine learning models in speaker identification. Spectrograms of the speech have been used as input in deep learning-based speaker identification using clean speech. However, the performance of speaker identification systems gets degraded under noisy conditions. Cochleograms have shown better results than spectrograms in deep learning-based speaker recognition under noisy and mismatched conditions. Moreover, hybrid convolutional neural network (CNN) and recurrent neural network (RNN) variants have shown better performance than CNN or RNN variants in recent studies. However, there is no attempt conducted to use a hybrid CNN and enhanced RNN variants in speaker identification using cochleogram input to enhance the performance under noisy and mismatched conditions. In this study, a speaker identification using hybrid CNN and the gated recurrent unit (GRU) is proposed for noisy conditions using cochleogram input. VoxCeleb1 audio dataset with real-world noises, white Gaussian noises (WGN) and without additive noises were employed for experiments. The experiment results and the comparison with existing works show that the proposed model performs better than other models in this study and existing works</span><span>.</span></p>

Spectral Restoration Based Speech Enhancement for Robust Speaker Identification

Spectral restoration based speech enhancement algorithms are used to enhance quality of noise masked speech for robust speaker identification. In presence of background noise, the performance of speaker identification systems can be severely deteriorated. The present study employed and evaluated the Minimum Mean-SquareError Short-Time Spectral Amplitude Estimators with modified a priori SNR estimate prior to speaker identification to improve performance of the speaker identification systems in presence of background noise. For speaker identification, Mel Frequency Cepstral coefficient and Vector Quantization is used to extract the speech features and to model the extracted features respectively. The experimental results showed significant improvement in speaker identification rates when spectral restoration based speech enhancement algorithms are used as a pre-processing step. The identification rates are found to be higher after employing the speech enhancement algorithms.

Wavelet based dynamic Mel Frequency Cepstral Coefficients (MFCC) and block truncation techniques for efficient speaker identification under narrowband noise conditions

Speaker identification strategies are well convincing in their performance when clean speeches are scrutinized. But the performance degrades when speech samples are corrupted by narrowband noise. Block truncation of the cepstral coefficients ensures that not all the features are affected by narrowband noise but it cannot reduce the extent of degradation. This work is focused towards improving the performance of speaker identification systems by block truncating the features which are subjected to wavelet processing. Wavelet decomposition divides the entire energy spectrum of the speech signal into bands corresponding to the number of levels of decomposition performed in the wavelet transformation thereby segregating the noise affected bands from other bands. In addition to that, wavelet filters provide the smoothening of the noisy speech signals which enhances the identification of the correct speaker. Dynamic Mel filtering of these wavelet coefficients followed by block truncation provides better identification, taking advantage of the fact that some filter bank coefficients remain unaffected by narrowband noise. The features are modeled by Gaussian mixture model - Universal background model (GMM-UBM) that serves as a generic one timed trained model. Speaker identification efficiency of 97.23% is achieved through this wavelet based dynamic MFCC technique which exhibits 7.58% improvement in speaker identification accuracy when compared with non wavelet based block truncation method. Key words: Wavelet decomposition, block truncation, Dynamic Mel Filtering Cepstral Coefficients (DMFCC), Gaussian mixture model - Universal background model (GMM-UBM), speaker identification.

Speaker Identification Within Whispered Speech Audio Streams

Whisper is an alternative speech production mode used by subjects in natural conversation to protect the privacy. Due to the profound differences between whisper and neutral speech in both excitation and vocal tract function, the performance of speaker identification systems trained with neutral speech degrades significantly. In this paper, a seamless neutral/whisper mismatched closed-set speaker recognition system is developed. First, performance characteristics of a neutral trained closed-set speaker ID system based on an Mel-frequency cepstral coefficient-Gaussian mixture model (MFCC-GMM) framework is considered. It is observed that for whisper speaker recognition, performance degradation is concentrated for only a subset of speakers. Next, it is shown that the performance loss for speaker identification in neutral/whisper mismatched conditions is focused on phonemes other than low-energy unvoiced consonants. In order to increase system performance for unvoiced consonants, an alternative feature extraction algorithm based on linear and exponential frequency scales is applied. The acoustic properties of misrecognized and correctly recognized whisper are analyzed in order to develop more effective processing schemes. A two-dimensional feature space is proposed in order to predict on which whispered utterances the system will perform poorly, with evaluations conducted to measure the quality of whispered speech. Finally, a system for seamless neutral/whisper speaker identification is proposed, resulting in an absolute improvement of 8.85%-10.30% for speaker recognition, with the best closed set speaker ID performance of 88.35% obtained for a total of 961 read whisper test utterances, and 83.84% using a total of 495 spontaneous whisper test utterances.

Enhancing speaker identification performance under the shouted talking condition using second-order circular hidden Markov models

It is known that the performance of speaker identification systems is high under the neutral talking condition; however, the performance deteriorates under the shouted talking condition. In this paper, second-order circular hidden Markov models (CHMM2s) have been proposed and implemented to enhance the performance of isolated-word text-dependent speaker identification systems under the shouted talking condition. Our results show that CHMM2s significantly improve speaker identification performance under such a condition compared to the first-order left-to-right hidden Markov models (LTRHMM1s), second-order left-to-right hidden Markov models (LTRHMM2s), and the first-order circular hidden Markov models (CHMM1s). Under the shouted talking condition, our results show that the average speaker identification performance is 23% based on LTRHMM1s, 59% based on LTRHMM2s, and 60% based on CHMM1s. On the other hand, the average speaker identification performance under the same talking condition based on CHMM2s is 72%.