Speaker Identification Applications Research Articles

Modified layer deep convolution neural network for text-independent speaker recognition

peaker recognition is the task of identifying the spokesman automatically using speaker-specific features. It has been a popular and most involved topic in the field of speech technology. This field opens a wide opportunity for research and finds its application in the areas such as forensics, authentication, security, etc. In this work, a modified deep-convolutional neural network structure has been proposed for speaker identification that has improved convolution, activation, and pooling layers along with Adam’s optimiser. The proposed architecture yielded the increase of prediction accuracy and reduction of Loss function when compared to the generic Convolutional Neural Network scheme. The execution of the proposed architecture is validated by various datasets and the outcomes show that the modified CNN performs better than the other state-of-the-art models regarding both accuracy (avg 99%) and loss function (avg 1%). From the analysis, it is found that the Modified-CNN suits the best for real-time speaker identification applications as the efficacy of the model does not degrade due to the effects of noise and interferences that are caused in the recording environment. Relevance of the work: Speaker Recognition is an area of interest in which ML and DL schemes, when combined, have the potential to make history in the areas of Automation and Authentication. Using a modified CNN can enhance the process by ignoring many issues such as false positives, background noise, and so on. This process can be expanded to create a Raga Identification and Therapy mechanism that can be used to treat diseases.

Voice Identification Using MFCC and Vector Quantization

The speaker identification is one of the fundamental problems in speech processing and voice modeling. The speaker identification applications include authentication in critical security systems and the accuracy of the selection. Large-scale voice recognition applications are a major challenge. Quick search in the speaker database requires fast, modern techniques and relies on artificial intelligence to achieve the desired results from the system. Many efforts are made to achieve this through the establishment of variable-based systems and the development of new methodologies for speaker identification. Speaker identification is the process of recognizing who is speaking using the characteristics extracted from the speech's waves like pitch, tone, and frequency. The speaker's models are created and saved in the system environment and used to verify the identity required by people accessing the systems, which allows access to various services that are controlled by voice, speaker identification involves two main parts: the first part is the feature extraction and the second part is the feature matching.

Speaker identification features extraction methods: A systematic review

Abstract Speaker Identification (SI) is the process of identifying the speaker from a given utterance by comparing the voice biometrics of the utterance with those utterance models stored beforehand. SI technologies are taken a new direction due to the advances in artificial intelligence and have been used widely in various domains. Feature extraction is one of the most important aspects of SI, which significantly influences the SI process and performance. This systematic review is conducted to identify, compare, and analyze various feature extraction approaches, methods, and algorithms of SI to provide a reference on feature extraction approaches for SI applications and future studies. The review was conducted according to Kitchenham systematic review methodology and guidelines, and provides an in-depth analysis on proposals and implementations of SI feature extraction methods discussed in the literature between year 2011 and 2106. Three research questions were determined and an initial set of 535 publications were identified to answer the questions. After applying exclusion criteria 160 related publications were shortlisted and reviewed in this paper; these papers were considered to answer the research questions. Results indicate that pure Mel-Frequency Cepstral Coefficients (MFCCs) based feature extraction approaches have been used more than any other approach. Furthermore, other MFCC variations, such as MFCC fusion and cleansing approaches, are proven to be very popular as well. This study identified that the current SI research trend is to develop a robust universal SI framework to address the important problems of SI such as adaptability, complexity, multi-lingual recognition, and noise robustness. The results presented in this research are based on past publications, citations, and number of implementations with citations being most relevant. This paper also presents the general process of SI.

Speaker Identification Using Data-Driven Score Classification

AbstractWe present a comparative evaluation of different classification algorithms for a fusion engine that is used in a speaker identity selection task. The fusion engine combines the scores from a number of classifiers, which uses the GMM-UBM approach to match speaker identity. The performances of the evaluated classification algorithms were examined in both the text-dependent and text-independent operation modes. The experimental results indicated a significant improvement in terms of speaker identification accuracy, which was approximately 7% and 14.5% for the text-dependent and the text-independent scenarios, respectively. We suggest the use of fusion with a discriminative algorithm such as a Support Vector Machine in a real-world speaker identification application where the text-independent scenario predominates based on the findings.

Glottal and Vocal Tract Characteristics of Voice Impersonators

Voice impersonators possess a flexible voice which allows them to imitate and create different voice identities. These impersonations present a challenge for forensic analysis and speaker identification systems. To better understand the phenomena underlying successful voice impersonation, we collected a database of synchronous speech and ElectroGlottoGraphic (EGG) signals from three voice impersonators each producing nine distinct voice identities. We analyzed glottal and vocal tract measures including F0, speech rate, vowel formant frequencies, and timing characteristics of the vocal folds. Our analysis confirmed that the impersonators modulated all four parameters in producing the voices, and provides a lower bound on the scale of variability that is available to impersonators. Importantly, vowel formant differences across voices were highly dependent on vowel category, showing that such effects cannot be captured by global transformations that ignore the linguistic parse. We address this issue through the development of a no-reference objective metric based on the vowel-dependent variance of the formants associated with each voice. This metric both ranks the impersonators natural voices highly, and correlates strongly with the results of a subjective listening test. Together, these results demonstrate the utility of voice variability data for the development of voice disguise detection and speaker identification applications.

Singer Identification Based on Spoken Data in Voice Characterization

Currently existing singer identification (SID) methods follow the framework of speaker identification (SPID), which requires that singing data be collected beforehand to establish each singer's voice characteristics. This framework, however, is unsuitable for many SID applications, because acquiring solo a cappella from each singer is usually not as feasible as collecting spoken data in SPID applications. Since a cappella data are difficult to acquire, many studies have tried to improve SID accuracies when only accompanied singing data are available for training; but, the improvements are not always satisfactory. Recognizing that spoken data are usually available easily, this work investigates the possibility of characterizing singers' voices using the spoken data instead of their singing data. Unfortunately, our experiment found it difficult to replace singing data fully by using spoken data in singer voice characterization, due to the significant difference between singing and speech voice for most people. Thus, we propose two alternative solutions based on the use of few singing data. The first solution aims at adapting a speech-derived model to cover singing voice characteristics. The second solution attempts to establish the relationships between speech and singing using a transformation, so that an unknown test singing clip can be converted into its speech counterpart and then identified using speech-derived models; or alternatively, training data can be converted from speech into singing to generate a singer model capable of matching test singing clips. Our experiments conducted using a 20-singer database validate the proposed solutions.

Automatic Singer Identification Based on Speech-Derived Models

Automatic singer identification (SNID) aims to determine who among a set of singers perform a given music recording. So far, most existing SNID methods follow a framework stemming from speaker identification (SPID) research, which models each person's characteristics using his/her voice data. This framework, however, is impractical in many SNID applications, because acquiring solo a cappella from each singer is usually not as feasible as collecting spoken data from each speaker in SPID applications. In view of the easy availability of spoken data, this work investigates the possibility of modeling singers' voices using spoken data instead of singing data. However, our experiment found it difficult to replace singing data fully by using spoken data in singer voice modeling, due to the significant difference between singing and speaking for most people. Thus, we propose an alternative solution based on the use of few available singing data. The idea is to modify speech-derived voice models using MAP adaptation on few singing data, so that the adapted voice models can cover performers' singing characteristics. Our experiments found that most of the singing clips can be correctly identified using the adapted voice models.