Speaker recognition with hybrid features from a deep belief network

Ali, Hazrat; Tran, SN; Benetos, Emmanouil; Garcez, Artur S. d’Avila

doi:10.1007/s00521-016-2501-7

Cited by 86 publications

(42 citation statements)

References 17 publications

(13 reference statements)

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“…The robot presented in this work accounts for obstacles only. An interesting future work is to make the robot more intelligent by using deep learning approaches such as those in [18], [19], [20], [21]. In this way, the robot can be trained for speech data so that the robot identifies voices and responds to both speech and image signaling.…”

Section: Discussionmentioning

confidence: 99%

Input Based Multiple Destination, Multiple Lines Following Robot with Obstacle Bypassing

Javed¹,

Hamid²,

Talha³

et al. 2018

ICST Transactions on Scalable Information Systems

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This paper presents a multiple destination, multiple lines following robot which takes input from user and then selects the defined colored line on the basis of given input and follows it until it reaches its destination. Each destination is defined with a specific colored line. The robot is intelligent enough to avoid collision with obstacles blocking its path, and to bypass the obstacle and then continue following its path. The robot has the ability to return to its initial position once the destination is achieved, thus completing a round trip successfully and getting ready to be employed again. The robot is able to turn across sharp turns as it follows the edge of the colored line and not the line itself. The robot is economical as least number of sensors are employed in the robot.

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Section: Discussionmentioning

confidence: 99%

Input Based Multiple Destination, Multiple Lines Following Robot with Obstacle Bypassing

Javed¹,

Hamid²,

Talha³

et al. 2018

ICST Transactions on Scalable Information Systems

Self Cite

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“…x is the mean of the first sample of size n, 2 x is the mean of the second example of a similar size, and SD pooled is the pooled standard deviation of the two samples given as", 22 1 2 pooled SD SD SD 2   (15) where "SD 1 is the standard deviation of the first sample of size n and SD 2 is the standard deviation of the second sample of equal size" [35]. Table 2.…”

Section: Speaker Identification Algorithm Based On Cascaded Gmm-dnn Amentioning

confidence: 99%

“…This experiment has been conducted to show the relevance of the proposed GMM-DNN as a classifier to enhance speaker identification performance in emotional environments and to compare it with other classifiers in the literature[19],[21],[22]. Matejka et.al[19] studied utilizing Deep Neural Network Bottleneck (DNN-BN) features together with MFCCs in the task of i-vector-based speaker recognition.Richardson et.al[21] presented the application of single DNN for both speaker recognition and language recognition using the "2013 Domain Adaptation Challenge speaker recognition (DAC13)" and the "NIST 2011 Language Recognition Evaluation (LRE11)" benchmarks.…”

mentioning

confidence: 99%

“…Matejka et.al[19] studied utilizing Deep Neural Network Bottleneck (DNN-BN) features together with MFCCs in the task of i-vector-based speaker recognition.Richardson et.al[21] presented the application of single DNN for both speaker recognition and language recognition using the "2013 Domain Adaptation Challenge speaker recognition (DAC13)" and the "NIST 2011 Language Recognition Evaluation (LRE11)" benchmarks. Ali et.al[22] studied the use of features from distinct levels of Deep Belief Network (DBN) to quantize the audio data into vectors of "audio-word counts".Table 6demonstrates speaker identification performance based on each of GMM-DNN and the three different prior work using the ESD. This table yields 5.8%, 3.9%, and 5.6% improvement rate in speaker identification performance based on the proposed classifier over that based on DNN-BN[19], single DNN[21], and DBN[22], respectively.…”

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confidence: 99%

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Novel cascaded Gaussian mixture model-deep neural network classifier for speaker identification in emotional talking environments

Shahin

Nassif

Hamsa

2018

Neural Comput & Applic

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This research is an effort to present an effective approach to enhance text-independent speaker identification performance in emotional talking environments based on novel classifier called cascaded Gaussian Mixture Model-Deep Neural Network (GMM-DNN). Our current work focuses on proposing, implementing and evaluating a new approach for speaker identification in emotional talking environments based on cascaded Gaussian Mixture Model-Deep Neural Network as a classifier. The results point out that the cascaded GMM-DNN classifier improves speaker identification performance at various emotions using two distinct speech databases:Emirati speech database (Arabic United Arab Emirates dataset) and "Speech Under Simulated and Actual Stress (SUSAS)" English dataset. The proposed classifier outperforms classical classifiers such as Multilayer Perceptron (MLP) and Support Vector Machine (SVM) in each dataset. Speaker identification performance that has been attained based on the cascaded GMM-DNN is similar to that acquired from subjective assessment by human listeners.2 Keywords: deep neural network; emotional talking environments; Gaussian mixture model; speaker identification. IntroductionSpeaker recognition and its sub divisional entities: "speaker identification and speaker verification" need to be redefined on the basis of talking environments as the neutral talking environment and the emotional talking environments. Speaker recognition performance faces drastic challenges, especially when the speaker identity going through the human-computer interface in emotional talking environments. Speaker recognition applications in security systems are widening their base into banking sector, customer care sector, criminal investigation and can be used as security control measure to remotely access a server or for access to confidential library files on a server. The process of "automatic speaker identification and verification" in stressful and emotional talking environments is a challenging area of research [1]. "Speaker identification" is comprised of two schemes in terms of sets: "closed set" and "open set" speaker identification. When the unknown speaker is presumed to be one among the database of known speakers, it becomes the scheme of a "closed set", while in the scheme of an "open set", the unfamiliar speaker might not necessarily be from the database of familiar speakers. Operational procedure divides speaker identification into "text-dependent", where the same text is uttered by the speaker in the training and testing phases and "text-independent", where different texts are uttered by the speaker during the training and testing phases [2]. 3 A perfect communication from a speaker depends not only on linguistic statements but also on the emotional aspects of the speaker. Identifying the emotional aspects of the speaker by the machine is still a challenge of the human-machine interface. Speech is always a perfect mix of linguistic notes linked with emotion along with its paralinguistic features. Emotion recognition from ...

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“…Deep learning (a sub branch of machine learning) algorithms have been popular for automatic recognition of digits and characters of different languages. Deep networks can be trained in supervised fashion requiring labels, or in an unsupervised way without requirements of labels [3], [4], [5]. In this work, we use an autoencoder network and a convolutional neural network (CNN) trained with 85% portion of the dataset and tested with the remaining 15% of the data.…”

Section: Introductionmentioning

confidence: 99%

Pioneer dataset and automatic recognition of Urdu handwritten characters using a deep autoencoder and convolutional neural network

et al. 2020

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Automatic recognition of Urdu handwritten digits and characters, is a challenging task. It has applications in postal address reading, bank's cheque processing, and digitization and preservation of handwritten manuscripts from old ages. While there exists a significant work for automatic recognition of handwritten English characters and other major languages of the world, the work done for Urdu language is extremely insufficient. This paper has two goals. Firstly, we introduce a pioneer dataset for handwritten digits and characters of Urdu, containing samples from more than 900 individuals. Secondly, we report results for automatic recognition of handwritten digits and characters as achieved by using deep auto-encoder network and convolutional neural network. More specifically, we use a two-layer and a three-layer deep autoencoder network and convolutional neural network and evaluate the two frameworks in terms of recognition accuracy. The proposed framework of deep autoencoder can successfully recognize digits and characters with an accuracy of 97% for digits only, 81% for characters only and 82% for both digits and characters simultaneously. In comparison, the framework of convolutional neural network has accuracy of 96.7% for digits only, 86.5% for characters only and 82.7% for both digits and characters simultaneously. These frameworks can serve as baselines for future research on Urdu handwritten text.

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Speaker recognition with hybrid features from a deep belief network

Cited by 86 publications

References 17 publications

Input Based Multiple Destination, Multiple Lines Following Robot with Obstacle Bypassing

Input Based Multiple Destination, Multiple Lines Following Robot with Obstacle Bypassing

Novel cascaded Gaussian mixture model-deep neural network classifier for speaker identification in emotional talking environments

Pioneer dataset and automatic recognition of Urdu handwritten characters using a deep autoencoder and convolutional neural network

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