Short Utterance Speaker Recognition Using Time-Delay Neural Network

Toruk, Muhammet Mesut; Gokay, Ramazan

doi:10.1109/ssd.2019.8893188

Cited by 10 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Voice recognition is the ability of a machine or program to receive and interpret dictation or to understand and carry out spoken commands [9][10]. The project developed is using these approach; -Voice recognition -Voice identification -Close set identification -Text dependent identification…”

Section: Fig 1: Voice Recognition Clasificationmentioning

confidence: 99%

Development of Academic Attendance System Using Voice Verification

Lazam

Saparon

2021

MJCS

View full text Add to dashboard Cite

Based on the fact that all Human Voices are different, where every individual’s voice contains unique characteristics that can be distinguished from others by using special analysis, voice verification can also be used for biometric recognition of individual. The aim of this project is to develop an automated attendance process captivating and student databases record maintaining using C++ programming and voice verification technique. The Input speech will undergo a series of voice recognition processes. The process starts with Feature extraction to obtain the attribute of voice. The features or specific characteristics for each voice are extracted by linear predictive coding technique (LPC) into feature vectors which later be used in speaker modeling. In speaker modeling stage the feature vectors of each speaker will be then processed by Gaussian Mixture Modeling (GMM) technique to generate speaker models and will be stored in a database. Verification process is done through template matching technique. Based on log-likelihood logic decision (template matching technique) with respect to the FAR (False Acceptance Rate) and FRR (False Rejection Rate), the identity of that speaker is accepted if the match is above the threshold. Once the verification result is accepted, the attendance in MySQL database for that speaker will be auto-updated.

show abstract

Section: Fig 1: Voice Recognition Clasificationmentioning

confidence: 99%

Development of Academic Attendance System Using Voice Verification

Lazam

Saparon

2021

MJCS

View full text Add to dashboard Cite

show abstract

“…At the beginning, identification mainly depends on the password authentication, then it has been developed to use biometric identification methods such as face recognition and fingerprint recognition. Articulatory features are also a biometric method that can be used for identification [8], [9], since speaking behaviors have personal habits and are difficult to imitate.…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Lip Motion Network for Text-Independent Speaker Recognition

Wang¹,

Wang

et al. 2020

Preprint

View full text Add to dashboard Cite

Lip motion reflects behavior characteristics of speakers, and thus can be used as a new kind of biometrics in speaker recognition. In the literature, lots of works used two-dimensional (2D) lip images to recognize speaker in a textdependent context. However, 2D lip easily suffers from various face orientations. To this end, in this work, we present a novel end-to-end 3D lip motion Network (3LMNet) by utilizing the sentence-level 3D lip motion (S3DLM) to recognize speakers in both the text-independent and text-dependent contexts. A new regional feedback module (RFM) is proposed to obtain attentions in different lip regions. Besides, prior knowledge of lip motion is investigated to complement RFM, where landmark-level and frame-level features are merged to form a better feature representation. Moreover, we present two methods, i.e., coordinate transformation and face posture correction to pre-process the LSD-AV dataset, which contains 68 speakers and 146 sentences per speaker. The evaluation results on this dataset demonstrate that our proposed 3LM-Net is superior to the baseline models, i.e., LSTM, VGG-16 and ResNet-34, and outperforms the state-of-the-art using 2D lip image as well as the 3D face. The code of this work is released at https://github.com/wutong18/Three-Dimensional-Lip-Motion-Network-for-Text-Independent-Speaker-Recognition.

show abstract

Real-Time Speech Recognition Using Convolutional Neural Network

Kakade

Salunke²

2022

Lecture Notes in Electrical Engineering

View full text Add to dashboard Cite

Short Utterance Speaker Recognition Using Time-Delay Neural Network

Cited by 10 publications

References 8 publications

Development of Academic Attendance System Using Voice Verification

Development of Academic Attendance System Using Voice Verification

Three-Dimensional Lip Motion Network for Text-Independent Speaker Recognition

Real-Time Speech Recognition Using Convolutional Neural Network

Contact Info

Product

Resources

About