Sorry, you need to enable JavaScript to visit this website.

Speaker Recognition and Characterization (SPE-SPKR)

Text-dependent Speaker Verification and RSR2015 Speech Corpus


RSR2015 (Robust Speaker Recognition 2015) is the largest publicly available speech corpus for text-dependent robust speaker recognition. The current release includes 151 hours of short duration utterances spoken by 300 speakers. RSR2015 is developed by the Human Language Technology (HLT) department at Institute for Infocomm Research (I2R) in Singapore. This newsletter describes RSR2015 corpus that addresses the reviving interest of text-dependent speaker recognition.

Paper Details

Authors:
Submitted On:
23 February 2016 - 1:44pm
Short Link:
Type:

Document Files

RSR2015_v2.pdf

(901)

Subscribe

[1] , "Text-dependent Speaker Verification and RSR2015 Speech Corpus", IEEE SigPort, 2014. [Online]. Available: http://sigport.org/54. Accessed: Dec. 08, 2019.
@article{54-14,
url = {http://sigport.org/54},
author = { },
publisher = {IEEE SigPort},
title = {Text-dependent Speaker Verification and RSR2015 Speech Corpus},
year = {2014} }
TY - EJOUR
T1 - Text-dependent Speaker Verification and RSR2015 Speech Corpus
AU -
PY - 2014
PB - IEEE SigPort
UR - http://sigport.org/54
ER -
. (2014). Text-dependent Speaker Verification and RSR2015 Speech Corpus. IEEE SigPort. http://sigport.org/54
, 2014. Text-dependent Speaker Verification and RSR2015 Speech Corpus. Available at: http://sigport.org/54.
. (2014). "Text-dependent Speaker Verification and RSR2015 Speech Corpus." Web.
1. . Text-dependent Speaker Verification and RSR2015 Speech Corpus [Internet]. IEEE SigPort; 2014. Available from : http://sigport.org/54

End-to-end Detection of Attacks to Automatic Speaker Recognizers with Time-attentive Light Convolutional Neural Networks


In this contribution, we introduce convolutional neural network architectures aiming at performing end-to-end detection of attacks to voice biometrics systems, i.e. the model provides scores corresponding to the likelihood of attack given general purpose time-frequency features obtained from speech. Microphone level attackers based on speech synthesis and voice conversion techniques are considered, along with presentation replay attacks.

Paper Details

Authors:
Joao Monteiro,Jahangir Alam,Tiago H. Falk
Submitted On:
6 November 2019 - 2:12pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

MLSP_E2E_SpoofingDetection.pdf

(9)

Subscribe

[1] Joao Monteiro,Jahangir Alam,Tiago H. Falk, "End-to-end Detection of Attacks to Automatic Speaker Recognizers with Time-attentive Light Convolutional Neural Networks", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4916. Accessed: Dec. 08, 2019.
@article{4916-19,
url = {http://sigport.org/4916},
author = {Joao Monteiro;Jahangir Alam;Tiago H. Falk },
publisher = {IEEE SigPort},
title = {End-to-end Detection of Attacks to Automatic Speaker Recognizers with Time-attentive Light Convolutional Neural Networks},
year = {2019} }
TY - EJOUR
T1 - End-to-end Detection of Attacks to Automatic Speaker Recognizers with Time-attentive Light Convolutional Neural Networks
AU - Joao Monteiro;Jahangir Alam;Tiago H. Falk
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4916
ER -
Joao Monteiro,Jahangir Alam,Tiago H. Falk. (2019). End-to-end Detection of Attacks to Automatic Speaker Recognizers with Time-attentive Light Convolutional Neural Networks. IEEE SigPort. http://sigport.org/4916
Joao Monteiro,Jahangir Alam,Tiago H. Falk, 2019. End-to-end Detection of Attacks to Automatic Speaker Recognizers with Time-attentive Light Convolutional Neural Networks. Available at: http://sigport.org/4916.
Joao Monteiro,Jahangir Alam,Tiago H. Falk. (2019). "End-to-end Detection of Attacks to Automatic Speaker Recognizers with Time-attentive Light Convolutional Neural Networks." Web.
1. Joao Monteiro,Jahangir Alam,Tiago H. Falk. End-to-end Detection of Attacks to Automatic Speaker Recognizers with Time-attentive Light Convolutional Neural Networks [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4916

Importance of analytic phase of the speech signal for detecting Replay attacks in automatic speaker verification systems


In this paper, the importance of analytic phase of the speech signal in automatic speaker verification systems is demonstrated in the context of replay spoof attacks. In order to accurately detect the replay spoof attacks, effective feature representations of speech signals are required to capture the distortion introduced due to the intermediate playback/recording devices, which is convolutive in nature.

Paper Details

Authors:
Shaik Mohammad Rafi B, Sri Rama Murty K
Submitted On:
16 May 2019 - 10:16pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Poster of ICASSP19

(51)

Subscribe

[1] Shaik Mohammad Rafi B, Sri Rama Murty K, "Importance of analytic phase of the speech signal for detecting Replay attacks in automatic speaker verification systems", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4543. Accessed: Dec. 08, 2019.
@article{4543-19,
url = {http://sigport.org/4543},
author = {Shaik Mohammad Rafi B; Sri Rama Murty K },
publisher = {IEEE SigPort},
title = {Importance of analytic phase of the speech signal for detecting Replay attacks in automatic speaker verification systems},
year = {2019} }
TY - EJOUR
T1 - Importance of analytic phase of the speech signal for detecting Replay attacks in automatic speaker verification systems
AU - Shaik Mohammad Rafi B; Sri Rama Murty K
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4543
ER -
Shaik Mohammad Rafi B, Sri Rama Murty K. (2019). Importance of analytic phase of the speech signal for detecting Replay attacks in automatic speaker verification systems. IEEE SigPort. http://sigport.org/4543
Shaik Mohammad Rafi B, Sri Rama Murty K, 2019. Importance of analytic phase of the speech signal for detecting Replay attacks in automatic speaker verification systems. Available at: http://sigport.org/4543.
Shaik Mohammad Rafi B, Sri Rama Murty K. (2019). "Importance of analytic phase of the speech signal for detecting Replay attacks in automatic speaker verification systems." Web.
1. Shaik Mohammad Rafi B, Sri Rama Murty K. Importance of analytic phase of the speech signal for detecting Replay attacks in automatic speaker verification systems [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4543

DEEP SPEAKER REPRESENTATION USING ORTHOGONAL DECOMPOSITION AND RECOMBINATION FOR SPEAKER VERIFICATION


Speech signal contains intrinsic and extrinsic variations such as accent, emotion, dialect, phoneme, speaking manner, noise, music, and reverberation. Some of these variations are unnecessary and are unspecified factors of variation. These factors lead to increased variability in speaker representation. In this paper, we assume that unspecified factors of variation exist in speaker representations, and we attempt to minimize variability in speaker representation.

Paper Details

Authors:
Insoo Kim, Kyuhong Kim, Jiwhan Kim, Changkyu Choi
Submitted On:
13 May 2019 - 2:29am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Poster_InsooKim.pdf

(75)

Subscribe

[1] Insoo Kim, Kyuhong Kim, Jiwhan Kim, Changkyu Choi, "DEEP SPEAKER REPRESENTATION USING ORTHOGONAL DECOMPOSITION AND RECOMBINATION FOR SPEAKER VERIFICATION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4477. Accessed: Dec. 08, 2019.
@article{4477-19,
url = {http://sigport.org/4477},
author = {Insoo Kim; Kyuhong Kim; Jiwhan Kim; Changkyu Choi },
publisher = {IEEE SigPort},
title = {DEEP SPEAKER REPRESENTATION USING ORTHOGONAL DECOMPOSITION AND RECOMBINATION FOR SPEAKER VERIFICATION},
year = {2019} }
TY - EJOUR
T1 - DEEP SPEAKER REPRESENTATION USING ORTHOGONAL DECOMPOSITION AND RECOMBINATION FOR SPEAKER VERIFICATION
AU - Insoo Kim; Kyuhong Kim; Jiwhan Kim; Changkyu Choi
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4477
ER -
Insoo Kim, Kyuhong Kim, Jiwhan Kim, Changkyu Choi. (2019). DEEP SPEAKER REPRESENTATION USING ORTHOGONAL DECOMPOSITION AND RECOMBINATION FOR SPEAKER VERIFICATION. IEEE SigPort. http://sigport.org/4477
Insoo Kim, Kyuhong Kim, Jiwhan Kim, Changkyu Choi, 2019. DEEP SPEAKER REPRESENTATION USING ORTHOGONAL DECOMPOSITION AND RECOMBINATION FOR SPEAKER VERIFICATION. Available at: http://sigport.org/4477.
Insoo Kim, Kyuhong Kim, Jiwhan Kim, Changkyu Choi. (2019). "DEEP SPEAKER REPRESENTATION USING ORTHOGONAL DECOMPOSITION AND RECOMBINATION FOR SPEAKER VERIFICATION." Web.
1. Insoo Kim, Kyuhong Kim, Jiwhan Kim, Changkyu Choi. DEEP SPEAKER REPRESENTATION USING ORTHOGONAL DECOMPOSITION AND RECOMBINATION FOR SPEAKER VERIFICATION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4477

Speaker Diarisation Using 2D Self-attentive Combination of Embeddings


Speaker diarisation systems often cluster audio segments using speaker embeddings such as i-vectors and d-vectors. Since different types of embeddings are often complementary, this paper proposes a generic framework to improve performance by combining them into a single embedding, referred to as a c-vector. This combination uses a 2-dimensional (2D) self-attentive structure, which extends the standard self-attentive layer by averaging not only across time but also across different types of embeddings.

Paper Details

Authors:
Guangzhi Sun, Chao Zhang, Phil Woodland
Submitted On:
12 May 2019 - 11:10am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

DiarisationPresentation3.pdf

(34)

Subscribe

[1] Guangzhi Sun, Chao Zhang, Phil Woodland, "Speaker Diarisation Using 2D Self-attentive Combination of Embeddings", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4465. Accessed: Dec. 08, 2019.
@article{4465-19,
url = {http://sigport.org/4465},
author = {Guangzhi Sun; Chao Zhang; Phil Woodland },
publisher = {IEEE SigPort},
title = {Speaker Diarisation Using 2D Self-attentive Combination of Embeddings},
year = {2019} }
TY - EJOUR
T1 - Speaker Diarisation Using 2D Self-attentive Combination of Embeddings
AU - Guangzhi Sun; Chao Zhang; Phil Woodland
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4465
ER -
Guangzhi Sun, Chao Zhang, Phil Woodland. (2019). Speaker Diarisation Using 2D Self-attentive Combination of Embeddings. IEEE SigPort. http://sigport.org/4465
Guangzhi Sun, Chao Zhang, Phil Woodland, 2019. Speaker Diarisation Using 2D Self-attentive Combination of Embeddings. Available at: http://sigport.org/4465.
Guangzhi Sun, Chao Zhang, Phil Woodland. (2019). "Speaker Diarisation Using 2D Self-attentive Combination of Embeddings." Web.
1. Guangzhi Sun, Chao Zhang, Phil Woodland. Speaker Diarisation Using 2D Self-attentive Combination of Embeddings [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4465

ATTENTIVE FILTERING NETWORKS FOR AUDIO REPLAY ATTACK DETECTION


An attacker may use a variety of techniques to fool an automatic speaker verification system into accepting them as a genuine user. Anti-spoofing methods meanwhile aim to make the system robust against such attacks. The ASVspoof 2017 Challenge focused specifically on replay attacks, with the intention of measuring the limits of replay attack detection as well as developing countermeasures against them.

Paper Details

Authors:
Cheng-I Lai, Alberto Abad, Korin Richmond, Junichi Yamagishi, Najim Dehak, Simon King
Submitted On:
9 May 2019 - 3:24pm
Short Link:
Type:
Event:
Presenter's Name:
Document Year:
Cite

Document Files

ICASSP poster (1).pdf

(37)

Subscribe

[1] Cheng-I Lai, Alberto Abad, Korin Richmond, Junichi Yamagishi, Najim Dehak, Simon King, "ATTENTIVE FILTERING NETWORKS FOR AUDIO REPLAY ATTACK DETECTION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4233. Accessed: Dec. 08, 2019.
@article{4233-19,
url = {http://sigport.org/4233},
author = {Cheng-I Lai; Alberto Abad; Korin Richmond; Junichi Yamagishi; Najim Dehak; Simon King },
publisher = {IEEE SigPort},
title = {ATTENTIVE FILTERING NETWORKS FOR AUDIO REPLAY ATTACK DETECTION},
year = {2019} }
TY - EJOUR
T1 - ATTENTIVE FILTERING NETWORKS FOR AUDIO REPLAY ATTACK DETECTION
AU - Cheng-I Lai; Alberto Abad; Korin Richmond; Junichi Yamagishi; Najim Dehak; Simon King
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4233
ER -
Cheng-I Lai, Alberto Abad, Korin Richmond, Junichi Yamagishi, Najim Dehak, Simon King. (2019). ATTENTIVE FILTERING NETWORKS FOR AUDIO REPLAY ATTACK DETECTION. IEEE SigPort. http://sigport.org/4233
Cheng-I Lai, Alberto Abad, Korin Richmond, Junichi Yamagishi, Najim Dehak, Simon King, 2019. ATTENTIVE FILTERING NETWORKS FOR AUDIO REPLAY ATTACK DETECTION. Available at: http://sigport.org/4233.
Cheng-I Lai, Alberto Abad, Korin Richmond, Junichi Yamagishi, Najim Dehak, Simon King. (2019). "ATTENTIVE FILTERING NETWORKS FOR AUDIO REPLAY ATTACK DETECTION." Web.
1. Cheng-I Lai, Alberto Abad, Korin Richmond, Junichi Yamagishi, Najim Dehak, Simon King. ATTENTIVE FILTERING NETWORKS FOR AUDIO REPLAY ATTACK DETECTION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4233

A DENOISING AUTOENCODER FOR SPEAKER RECOGNITION. RESULTS ON THE MCE 2018 CHALLENGE


We propose a Denoising Autoencoder (DAE) for speaker recognition, trained to map each individual ivector to the mean of all ivectors belonging to that particular speaker. The aim of this DAE is to compensate for inter-session variability and increase the discriminative power of the ivectors prior to PLDA scoring. We test the proposed approach on the MCE 2018 1st Multi-target speaker detection and identification Challenge Evaluation. This evaluation presents a call-center fraud detection scenario: given a speech segment, detect if it belongs to any of the speakers in a blacklist.

Paper Details

Authors:
Roberto Font
Submitted On:
9 May 2019 - 5:56am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

ICASSP-2019_RobertoFont.pdf

(54)

Subscribe

[1] Roberto Font, "A DENOISING AUTOENCODER FOR SPEAKER RECOGNITION. RESULTS ON THE MCE 2018 CHALLENGE", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4180. Accessed: Dec. 08, 2019.
@article{4180-19,
url = {http://sigport.org/4180},
author = {Roberto Font },
publisher = {IEEE SigPort},
title = {A DENOISING AUTOENCODER FOR SPEAKER RECOGNITION. RESULTS ON THE MCE 2018 CHALLENGE},
year = {2019} }
TY - EJOUR
T1 - A DENOISING AUTOENCODER FOR SPEAKER RECOGNITION. RESULTS ON THE MCE 2018 CHALLENGE
AU - Roberto Font
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4180
ER -
Roberto Font. (2019). A DENOISING AUTOENCODER FOR SPEAKER RECOGNITION. RESULTS ON THE MCE 2018 CHALLENGE. IEEE SigPort. http://sigport.org/4180
Roberto Font, 2019. A DENOISING AUTOENCODER FOR SPEAKER RECOGNITION. RESULTS ON THE MCE 2018 CHALLENGE. Available at: http://sigport.org/4180.
Roberto Font. (2019). "A DENOISING AUTOENCODER FOR SPEAKER RECOGNITION. RESULTS ON THE MCE 2018 CHALLENGE." Web.
1. Roberto Font. A DENOISING AUTOENCODER FOR SPEAKER RECOGNITION. RESULTS ON THE MCE 2018 CHALLENGE [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4180

Deep Speaker Embedding Learning with Multi-Level Pooling for Text-Independent Speaker Verification


This paper aims to improve the widely used deep speaker embedding x-vector model. We propose the following improvements: (1) a hybrid neural network structure using both time delay neural network (TDNN) and long short-term memory neural networks (LSTM) to generate complementary speaker information at different levels; (2) a multi-level pooling strategy to collect speaker information from both TDNN and LSTM layers; (3) a regularization scheme on the speaker embedding extraction layer to make the extracted embeddings suitable for the following fusion step.

Paper Details

Authors:
Yun Tang, Guohong Ding, Jing Huang, Xiaodong He, Bowen Zhou
Submitted On:
8 May 2019 - 2:09pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

ICASSP2019_poster.pdf

(54)

Subscribe

[1] Yun Tang, Guohong Ding, Jing Huang, Xiaodong He, Bowen Zhou, "Deep Speaker Embedding Learning with Multi-Level Pooling for Text-Independent Speaker Verification", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4126. Accessed: Dec. 08, 2019.
@article{4126-19,
url = {http://sigport.org/4126},
author = {Yun Tang; Guohong Ding; Jing Huang; Xiaodong He; Bowen Zhou },
publisher = {IEEE SigPort},
title = {Deep Speaker Embedding Learning with Multi-Level Pooling for Text-Independent Speaker Verification},
year = {2019} }
TY - EJOUR
T1 - Deep Speaker Embedding Learning with Multi-Level Pooling for Text-Independent Speaker Verification
AU - Yun Tang; Guohong Ding; Jing Huang; Xiaodong He; Bowen Zhou
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4126
ER -
Yun Tang, Guohong Ding, Jing Huang, Xiaodong He, Bowen Zhou. (2019). Deep Speaker Embedding Learning with Multi-Level Pooling for Text-Independent Speaker Verification. IEEE SigPort. http://sigport.org/4126
Yun Tang, Guohong Ding, Jing Huang, Xiaodong He, Bowen Zhou, 2019. Deep Speaker Embedding Learning with Multi-Level Pooling for Text-Independent Speaker Verification. Available at: http://sigport.org/4126.
Yun Tang, Guohong Ding, Jing Huang, Xiaodong He, Bowen Zhou. (2019). "Deep Speaker Embedding Learning with Multi-Level Pooling for Text-Independent Speaker Verification." Web.
1. Yun Tang, Guohong Ding, Jing Huang, Xiaodong He, Bowen Zhou. Deep Speaker Embedding Learning with Multi-Level Pooling for Text-Independent Speaker Verification [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4126

PRE-TRAINING OF SPEAKER EMBEDDINGS FOR LOW-LATENCY SPEAKER CHANGE DETECTION IN BROADCAST NEWS


In this work, we investigate pre-training of neural network based speaker embeddings for low-latency speaker change detection. Our proposed system takes two speech segments, generates embeddings using shared Siamese layers and then classifies the concatenated embeddings depending on whether they are spoken by the same speaker. We investigate gender classification, contrastive loss and triplet loss based pre-training of the embedding layers and also joint training of the embedding layers along with a same/different classifier.

Paper Details

Authors:
Samuel Thomas, Mark Hasegawa-Johnson, Michael Picheny
Submitted On:
8 May 2019 - 11:35am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

poster_final_ledaSari.pdf

(47)

Subscribe

[1] Samuel Thomas, Mark Hasegawa-Johnson, Michael Picheny, "PRE-TRAINING OF SPEAKER EMBEDDINGS FOR LOW-LATENCY SPEAKER CHANGE DETECTION IN BROADCAST NEWS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4118. Accessed: Dec. 08, 2019.
@article{4118-19,
url = {http://sigport.org/4118},
author = {Samuel Thomas; Mark Hasegawa-Johnson; Michael Picheny },
publisher = {IEEE SigPort},
title = {PRE-TRAINING OF SPEAKER EMBEDDINGS FOR LOW-LATENCY SPEAKER CHANGE DETECTION IN BROADCAST NEWS},
year = {2019} }
TY - EJOUR
T1 - PRE-TRAINING OF SPEAKER EMBEDDINGS FOR LOW-LATENCY SPEAKER CHANGE DETECTION IN BROADCAST NEWS
AU - Samuel Thomas; Mark Hasegawa-Johnson; Michael Picheny
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4118
ER -
Samuel Thomas, Mark Hasegawa-Johnson, Michael Picheny. (2019). PRE-TRAINING OF SPEAKER EMBEDDINGS FOR LOW-LATENCY SPEAKER CHANGE DETECTION IN BROADCAST NEWS. IEEE SigPort. http://sigport.org/4118
Samuel Thomas, Mark Hasegawa-Johnson, Michael Picheny, 2019. PRE-TRAINING OF SPEAKER EMBEDDINGS FOR LOW-LATENCY SPEAKER CHANGE DETECTION IN BROADCAST NEWS. Available at: http://sigport.org/4118.
Samuel Thomas, Mark Hasegawa-Johnson, Michael Picheny. (2019). "PRE-TRAINING OF SPEAKER EMBEDDINGS FOR LOW-LATENCY SPEAKER CHANGE DETECTION IN BROADCAST NEWS." Web.
1. Samuel Thomas, Mark Hasegawa-Johnson, Michael Picheny. PRE-TRAINING OF SPEAKER EMBEDDINGS FOR LOW-LATENCY SPEAKER CHANGE DETECTION IN BROADCAST NEWS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4118

FORMANT-GAPS FEATURES FOR SPEAKER VERIFICATION USING WHISPERED SPEECH


In this work, we propose a new feature based on formants for whispered speaker verification (SV) task, where neutral data is used for enrollment and whispered recordings are used for test. Such a mismatch between enrollment and test often degrades the performance of whispered SV systems due to the difference in acoustic characteristics of whispered and neutral speech. We hypothesize that the proposed formant and formant gap (F oG) features are more invariant to the modes of speech in capturing speaker specific information

Paper Details

Authors:
Abinay Reddy Naini, Achuth Rao MV, Prasanta Kumar Ghosh
Submitted On:
8 May 2019 - 5:23am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

wsv_poster.pdf

(40)

Subscribe

[1] Abinay Reddy Naini, Achuth Rao MV, Prasanta Kumar Ghosh, "FORMANT-GAPS FEATURES FOR SPEAKER VERIFICATION USING WHISPERED SPEECH", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4056. Accessed: Dec. 08, 2019.
@article{4056-19,
url = {http://sigport.org/4056},
author = {Abinay Reddy Naini; Achuth Rao MV; Prasanta Kumar Ghosh },
publisher = {IEEE SigPort},
title = {FORMANT-GAPS FEATURES FOR SPEAKER VERIFICATION USING WHISPERED SPEECH},
year = {2019} }
TY - EJOUR
T1 - FORMANT-GAPS FEATURES FOR SPEAKER VERIFICATION USING WHISPERED SPEECH
AU - Abinay Reddy Naini; Achuth Rao MV; Prasanta Kumar Ghosh
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4056
ER -
Abinay Reddy Naini, Achuth Rao MV, Prasanta Kumar Ghosh. (2019). FORMANT-GAPS FEATURES FOR SPEAKER VERIFICATION USING WHISPERED SPEECH. IEEE SigPort. http://sigport.org/4056
Abinay Reddy Naini, Achuth Rao MV, Prasanta Kumar Ghosh, 2019. FORMANT-GAPS FEATURES FOR SPEAKER VERIFICATION USING WHISPERED SPEECH. Available at: http://sigport.org/4056.
Abinay Reddy Naini, Achuth Rao MV, Prasanta Kumar Ghosh. (2019). "FORMANT-GAPS FEATURES FOR SPEAKER VERIFICATION USING WHISPERED SPEECH." Web.
1. Abinay Reddy Naini, Achuth Rao MV, Prasanta Kumar Ghosh. FORMANT-GAPS FEATURES FOR SPEAKER VERIFICATION USING WHISPERED SPEECH [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4056

Pages