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Speech Processing

A CLASSIFICATION-AIDED FRAMEWORK FOR NON-INTRUSIVE SPEECH QUALITY ASSESSMENT

Donald Williamson

Objective metrics, such as the perceptual evaluation of speech quality (PESQ) have become standard measures for evaluating speech. These metrics enable efficient and costless evaluations, where ratings are often computed by comparing a degraded speech signal to its underlying clean reference signal. Reference-based metrics, however, cannot be used to evaluate real-world signals that have inaccessible references. This project develops a nonintrusive framework for evaluating the perceptual quality of noisy and enhanced speech.

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Authors:
Donald Williamson
Xuan Dong, Donald Williamson
Submitted On:
21 October 2019 - 2:28pm
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[1] Xuan Dong, Donald Williamson, "A CLASSIFICATION-AIDED FRAMEWORK FOR NON-INTRUSIVE SPEECH QUALITY ASSESSMENT", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4883. Accessed: Jan. 20, 2020.
@article{4883-19,
url = {http://sigport.org/4883},
author = {Xuan Dong; Donald Williamson },
publisher = {IEEE SigPort},
title = {A CLASSIFICATION-AIDED FRAMEWORK FOR NON-INTRUSIVE SPEECH QUALITY ASSESSMENT},
year = {2019} }
TY - EJOUR
T1 - A CLASSIFICATION-AIDED FRAMEWORK FOR NON-INTRUSIVE SPEECH QUALITY ASSESSMENT
AU - Xuan Dong; Donald Williamson
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4883
ER -
Xuan Dong, Donald Williamson. (2019). A CLASSIFICATION-AIDED FRAMEWORK FOR NON-INTRUSIVE SPEECH QUALITY ASSESSMENT. IEEE SigPort. http://sigport.org/4883
Xuan Dong, Donald Williamson, 2019. A CLASSIFICATION-AIDED FRAMEWORK FOR NON-INTRUSIVE SPEECH QUALITY ASSESSMENT. Available at: http://sigport.org/4883.
Xuan Dong, Donald Williamson. (2019). "A CLASSIFICATION-AIDED FRAMEWORK FOR NON-INTRUSIVE SPEECH QUALITY ASSESSMENT." Web.
1. Xuan Dong, Donald Williamson. A CLASSIFICATION-AIDED FRAMEWORK FOR NON-INTRUSIVE SPEECH QUALITY ASSESSMENT [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4883

Speech Landmark Bigrams for Depression Detection from Naturalistic Smartphone Speech

Zhaocheng Huang

Detection of depression from speech has attracted significant research attention in recent years but remains a challenge, particularly for speech from diverse smartphones in natural environments. This paper proposes two sets of novel features based on speech landmark bigrams associated with abrupt speech articulatory events for depression detection from smartphone audio recordings. Combined with techniques adapted from natural language text processing, the proposed features further exploit landmark bigrams by discovering latent articulatory events.

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Authors:
Zhaocheng Huang
Zhaocheng Huang, Julien Epps, Dale Joachim
Submitted On:
6 June 2019 - 4:42am
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[1] Zhaocheng Huang, Julien Epps, Dale Joachim, "Speech Landmark Bigrams for Depression Detection from Naturalistic Smartphone Speech", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4565. Accessed: Jan. 20, 2020.
@article{4565-19,
url = {http://sigport.org/4565},
author = {Zhaocheng Huang; Julien Epps; Dale Joachim },
publisher = {IEEE SigPort},
title = {Speech Landmark Bigrams for Depression Detection from Naturalistic Smartphone Speech},
year = {2019} }
TY - EJOUR
T1 - Speech Landmark Bigrams for Depression Detection from Naturalistic Smartphone Speech
AU - Zhaocheng Huang; Julien Epps; Dale Joachim
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4565
ER -
Zhaocheng Huang, Julien Epps, Dale Joachim. (2019). Speech Landmark Bigrams for Depression Detection from Naturalistic Smartphone Speech. IEEE SigPort. http://sigport.org/4565
Zhaocheng Huang, Julien Epps, Dale Joachim, 2019. Speech Landmark Bigrams for Depression Detection from Naturalistic Smartphone Speech. Available at: http://sigport.org/4565.
Zhaocheng Huang, Julien Epps, Dale Joachim. (2019). "Speech Landmark Bigrams for Depression Detection from Naturalistic Smartphone Speech." Web.
1. Zhaocheng Huang, Julien Epps, Dale Joachim. Speech Landmark Bigrams for Depression Detection from Naturalistic Smartphone Speech [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4565

Adversarial Speaker Adaptation

Zhong Meng
Jinyu Li

We propose a novel adversarial speaker adaptation (ASA) scheme, in which adversarial learning is applied to regularize the distribution of deep hidden features in a speaker-dependent (SD) deep neural network (DNN) acoustic model to be close to that of a fixed speaker-independent (SI) DNN acoustic model during adaptation. An additional discriminator network is introduced to distinguish the deep features generated by the SD model from those produced by the SI model.

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Authors:
Zhong Meng
Jinyu Li
Zhong Meng, Jinyu Li, Yifan Gong
Submitted On:
12 May 2019 - 9:26pm
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[1] Zhong Meng, Jinyu Li, Yifan Gong, "Adversarial Speaker Adaptation", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4475. Accessed: Jan. 20, 2020.
@article{4475-19,
url = {http://sigport.org/4475},
author = {Zhong Meng; Jinyu Li; Yifan Gong },
publisher = {IEEE SigPort},
title = {Adversarial Speaker Adaptation},
year = {2019} }
TY - EJOUR
T1 - Adversarial Speaker Adaptation
AU - Zhong Meng; Jinyu Li; Yifan Gong
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4475
ER -
Zhong Meng, Jinyu Li, Yifan Gong. (2019). Adversarial Speaker Adaptation. IEEE SigPort. http://sigport.org/4475
Zhong Meng, Jinyu Li, Yifan Gong, 2019. Adversarial Speaker Adaptation. Available at: http://sigport.org/4475.
Zhong Meng, Jinyu Li, Yifan Gong. (2019). "Adversarial Speaker Adaptation." Web.
1. Zhong Meng, Jinyu Li, Yifan Gong. Adversarial Speaker Adaptation [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4475

Attentive Adversarial Learning for Domain-Invariant Training

Zhong Meng
Jinyu Li

Adversarial domain-invariant training (ADIT) proves to be effective in suppressing the effects of domain variability in acoustic modeling and has led to improved performance in automatic speech recognition (ASR). In ADIT, an auxiliary domain classifier takes in equally-weighted deep features from a deep neural network (DNN) acoustic model and is trained to improve their domain-invariance by optimizing an adversarial loss function.

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Authors:
Zhong Meng
Jinyu Li
Zhong Meng, Jinyu Li, Yifan Gong
Submitted On:
12 May 2019 - 9:03pm
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[1] Zhong Meng, Jinyu Li, Yifan Gong, "Attentive Adversarial Learning for Domain-Invariant Training", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4474. Accessed: Jan. 20, 2020.
@article{4474-19,
url = {http://sigport.org/4474},
author = {Zhong Meng; Jinyu Li; Yifan Gong },
publisher = {IEEE SigPort},
title = {Attentive Adversarial Learning for Domain-Invariant Training},
year = {2019} }
TY - EJOUR
T1 - Attentive Adversarial Learning for Domain-Invariant Training
AU - Zhong Meng; Jinyu Li; Yifan Gong
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4474
ER -
Zhong Meng, Jinyu Li, Yifan Gong. (2019). Attentive Adversarial Learning for Domain-Invariant Training. IEEE SigPort. http://sigport.org/4474
Zhong Meng, Jinyu Li, Yifan Gong, 2019. Attentive Adversarial Learning for Domain-Invariant Training. Available at: http://sigport.org/4474.
Zhong Meng, Jinyu Li, Yifan Gong. (2019). "Attentive Adversarial Learning for Domain-Invariant Training." Web.
1. Zhong Meng, Jinyu Li, Yifan Gong. Attentive Adversarial Learning for Domain-Invariant Training [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4474

Adversarial Speaker Verification

Zhong Meng
Yong Zhao
Jinyu Li

The use of deep networks to extract embeddings for speaker recognition has proven successfully. However, such embeddings are susceptible to performance degradation due to the mismatches among the training, enrollment, and test conditions. In this work, we propose an adversarial speaker verification (ASV) scheme to learn the condition-invariant deep embedding via adversarial multi-task training. In ASV, a speaker classification network and a condition identification network are jointly optimized to minimize the speaker classification loss and simultaneously mini-maximize the condition loss.

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Authors:
Zhong Meng
Yong Zhao
Jinyu Li
Zhong Meng, Yong Zhao, Jinyu Li, Yifan Gong
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12 May 2019 - 9:24pm
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[1] Zhong Meng, Yong Zhao, Jinyu Li, Yifan Gong, "Adversarial Speaker Verification", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4473. Accessed: Jan. 20, 2020.
@article{4473-19,
url = {http://sigport.org/4473},
author = {Zhong Meng; Yong Zhao; Jinyu Li; Yifan Gong },
publisher = {IEEE SigPort},
title = {Adversarial Speaker Verification},
year = {2019} }
TY - EJOUR
T1 - Adversarial Speaker Verification
AU - Zhong Meng; Yong Zhao; Jinyu Li; Yifan Gong
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4473
ER -
Zhong Meng, Yong Zhao, Jinyu Li, Yifan Gong. (2019). Adversarial Speaker Verification. IEEE SigPort. http://sigport.org/4473
Zhong Meng, Yong Zhao, Jinyu Li, Yifan Gong, 2019. Adversarial Speaker Verification. Available at: http://sigport.org/4473.
Zhong Meng, Yong Zhao, Jinyu Li, Yifan Gong. (2019). "Adversarial Speaker Verification." Web.
1. Zhong Meng, Yong Zhao, Jinyu Li, Yifan Gong. Adversarial Speaker Verification [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4473

Conditional Teacher-Student Learning

Zhong Meng
Jinyu Li
Yong Zhao

The teacher-student (T/S) learning has been shown to be effective for a variety of problems such as domain adaptation and model compression. One shortcoming of the T/S learning is that a teacher model, not always perfect, sporadically produces wrong guidance in form of posterior probabilities that misleads the student model towards a suboptimal performance.

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Authors:
Zhong Meng
Jinyu Li
Yong Zhao
Zhong Meng, Jinyu Li, Yong Zhao, Yifan Gong
Submitted On:
12 May 2019 - 9:23pm
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[1] Zhong Meng, Jinyu Li, Yong Zhao, Yifan Gong, "Conditional Teacher-Student Learning", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4472. Accessed: Jan. 20, 2020.
@article{4472-19,
url = {http://sigport.org/4472},
author = {Zhong Meng; Jinyu Li; Yong Zhao; Yifan Gong },
publisher = {IEEE SigPort},
title = {Conditional Teacher-Student Learning},
year = {2019} }
TY - EJOUR
T1 - Conditional Teacher-Student Learning
AU - Zhong Meng; Jinyu Li; Yong Zhao; Yifan Gong
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4472
ER -
Zhong Meng, Jinyu Li, Yong Zhao, Yifan Gong. (2019). Conditional Teacher-Student Learning. IEEE SigPort. http://sigport.org/4472
Zhong Meng, Jinyu Li, Yong Zhao, Yifan Gong, 2019. Conditional Teacher-Student Learning. Available at: http://sigport.org/4472.
Zhong Meng, Jinyu Li, Yong Zhao, Yifan Gong. (2019). "Conditional Teacher-Student Learning." Web.
1. Zhong Meng, Jinyu Li, Yong Zhao, Yifan Gong. Conditional Teacher-Student Learning [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4472

Role Specific Lattice Rescoring for Speaker Role Recognition from Speech Recognition Outputs

Nikolaos Flemotomos
Panayiotis Georgiou

The language patterns followed by different speakers who play specific roles in conversational interactions provide valuable cues for the task of Speaker Role Recognition (SRR). Given the speech signal, existing algorithms typically try to find such patterns in the output of an Automatic Speech Recognition (ASR) system. In this work we propose an alternative way of revealing role-specific linguistic characteristics, by making use of role-specific ASR outputs, which are built by suitably rescoring the lattice produced after a first pass of ASR decoding.

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Authors:
Nikolaos Flemotomos
Panayiotis Georgiou
David C. Atkins, Shrikanth Narayanan
Submitted On:
9 May 2019 - 3:15pm
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[1] David C. Atkins, Shrikanth Narayanan, "Role Specific Lattice Rescoring for Speaker Role Recognition from Speech Recognition Outputs", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4232. Accessed: Jan. 20, 2020.
@article{4232-19,
url = {http://sigport.org/4232},
author = {David C. Atkins; Shrikanth Narayanan },
publisher = {IEEE SigPort},
title = {Role Specific Lattice Rescoring for Speaker Role Recognition from Speech Recognition Outputs},
year = {2019} }
TY - EJOUR
T1 - Role Specific Lattice Rescoring for Speaker Role Recognition from Speech Recognition Outputs
AU - David C. Atkins; Shrikanth Narayanan
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4232
ER -
David C. Atkins, Shrikanth Narayanan. (2019). Role Specific Lattice Rescoring for Speaker Role Recognition from Speech Recognition Outputs. IEEE SigPort. http://sigport.org/4232
David C. Atkins, Shrikanth Narayanan, 2019. Role Specific Lattice Rescoring for Speaker Role Recognition from Speech Recognition Outputs. Available at: http://sigport.org/4232.
David C. Atkins, Shrikanth Narayanan. (2019). "Role Specific Lattice Rescoring for Speaker Role Recognition from Speech Recognition Outputs." Web.
1. David C. Atkins, Shrikanth Narayanan. Role Specific Lattice Rescoring for Speaker Role Recognition from Speech Recognition Outputs [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4232

Retrieving Speech Samples with Similar Emotional Content Using a Triplet Loss Function

John Harvill
Mohammed Abdelwahab

The ability to identify speech with similar emotional content is valuable to many applications, including speech retrieval, surveillance, and emotional speech synthesis. While current formulations in speech emotion recognition based on classification or regression are not appropriate for this task, solutions based on preference learning offer appealing approaches for this task. This paper aims to find speech samples that are emotionally similar to an anchor speech sample provided as a query. This novel formulation opens interesting research questions.

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Authors:
John Harvill
Mohammed Abdelwahab
Reza Lotfian, Carlos Busso
Submitted On:
9 May 2019 - 12:32pm
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[1] Reza Lotfian, Carlos Busso, "Retrieving Speech Samples with Similar Emotional Content Using a Triplet Loss Function", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4222. Accessed: Jan. 20, 2020.
@article{4222-19,
url = {http://sigport.org/4222},
author = {Reza Lotfian; Carlos Busso },
publisher = {IEEE SigPort},
title = {Retrieving Speech Samples with Similar Emotional Content Using a Triplet Loss Function},
year = {2019} }
TY - EJOUR
T1 - Retrieving Speech Samples with Similar Emotional Content Using a Triplet Loss Function
AU - Reza Lotfian; Carlos Busso
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4222
ER -
Reza Lotfian, Carlos Busso. (2019). Retrieving Speech Samples with Similar Emotional Content Using a Triplet Loss Function. IEEE SigPort. http://sigport.org/4222
Reza Lotfian, Carlos Busso, 2019. Retrieving Speech Samples with Similar Emotional Content Using a Triplet Loss Function. Available at: http://sigport.org/4222.
Reza Lotfian, Carlos Busso. (2019). "Retrieving Speech Samples with Similar Emotional Content Using a Triplet Loss Function." Web.
1. Reza Lotfian, Carlos Busso. Retrieving Speech Samples with Similar Emotional Content Using a Triplet Loss Function [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4222

SPEAKER AGNOSTIC FOREGROUND SPEECH DETECTION FROM AUDIO RECORDINGS 
IN WORKPLACE SETTINGS FROM WEARABLE RECORDERS


Amrutha Nadarajan

Audio-signal acquisition as part of wearable sensing adds an important dimension for applications such as understanding human behaviors. As part of a large study on work place behaviors, we collected audio data from individual hospital staff using custom wearable recorders. The audio features collected were limited to preserve privacy of the interactions in the hospital. A first step towards audio processing is to identify the foreground speech of the person wearing the audio badge.

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Authors:
Amrutha Nadarajan
Amrutha Nadarajan, Krishna Somandepalli, Shrikanth S. Narayanan
Submitted On:
9 May 2019 - 12:29am
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SPEAKER AGNOSTIC FOREGROUND SPEECH DETECTION FROM AUDIO RECORDINGS 
IN WORKPLACE SETTINGS FROM WEARABLE RECORDERS


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[1] Amrutha Nadarajan, Krishna Somandepalli, Shrikanth S. Narayanan, "SPEAKER AGNOSTIC FOREGROUND SPEECH DETECTION FROM AUDIO RECORDINGS 
IN WORKPLACE SETTINGS FROM WEARABLE RECORDERS
", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4146. Accessed: Jan. 20, 2020.
@article{4146-19,
url = {http://sigport.org/4146},
author = {Amrutha Nadarajan; Krishna Somandepalli; Shrikanth S. Narayanan },
publisher = {IEEE SigPort},
title = {SPEAKER AGNOSTIC FOREGROUND SPEECH DETECTION FROM AUDIO RECORDINGS 
IN WORKPLACE SETTINGS FROM WEARABLE RECORDERS
},
year = {2019} }
TY - EJOUR
T1 - SPEAKER AGNOSTIC FOREGROUND SPEECH DETECTION FROM AUDIO RECORDINGS 
IN WORKPLACE SETTINGS FROM WEARABLE RECORDERS

AU - Amrutha Nadarajan; Krishna Somandepalli; Shrikanth S. Narayanan
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4146
ER -
Amrutha Nadarajan, Krishna Somandepalli, Shrikanth S. Narayanan. (2019). SPEAKER AGNOSTIC FOREGROUND SPEECH DETECTION FROM AUDIO RECORDINGS 
IN WORKPLACE SETTINGS FROM WEARABLE RECORDERS
. IEEE SigPort. http://sigport.org/4146
Amrutha Nadarajan, Krishna Somandepalli, Shrikanth S. Narayanan, 2019. SPEAKER AGNOSTIC FOREGROUND SPEECH DETECTION FROM AUDIO RECORDINGS 
IN WORKPLACE SETTINGS FROM WEARABLE RECORDERS
. Available at: http://sigport.org/4146.
Amrutha Nadarajan, Krishna Somandepalli, Shrikanth S. Narayanan. (2019). "SPEAKER AGNOSTIC FOREGROUND SPEECH DETECTION FROM AUDIO RECORDINGS 
IN WORKPLACE SETTINGS FROM WEARABLE RECORDERS
." Web.
1. Amrutha Nadarajan, Krishna Somandepalli, Shrikanth S. Narayanan. SPEAKER AGNOSTIC FOREGROUND SPEECH DETECTION FROM AUDIO RECORDINGS 
IN WORKPLACE SETTINGS FROM WEARABLE RECORDERS
 [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4146

MULTI-BAND PIT AND MODEL INTEGRATION FOR IMPROVED MULTI-CHANNEL SPEECH SEPARATION

lianwu chen

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Authors:
lianwu chen
Submitted On:
8 May 2019 - 10:14pm
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[1] , "MULTI-BAND PIT AND MODEL INTEGRATION FOR IMPROVED MULTI-CHANNEL SPEECH SEPARATION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4141. Accessed: Jan. 20, 2020.
@article{4141-19,
url = {http://sigport.org/4141},
author = { },
publisher = {IEEE SigPort},
title = {MULTI-BAND PIT AND MODEL INTEGRATION FOR IMPROVED MULTI-CHANNEL SPEECH SEPARATION},
year = {2019} }
TY - EJOUR
T1 - MULTI-BAND PIT AND MODEL INTEGRATION FOR IMPROVED MULTI-CHANNEL SPEECH SEPARATION
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4141
ER -
. (2019). MULTI-BAND PIT AND MODEL INTEGRATION FOR IMPROVED MULTI-CHANNEL SPEECH SEPARATION. IEEE SigPort. http://sigport.org/4141
, 2019. MULTI-BAND PIT AND MODEL INTEGRATION FOR IMPROVED MULTI-CHANNEL SPEECH SEPARATION. Available at: http://sigport.org/4141.
. (2019). "MULTI-BAND PIT AND MODEL INTEGRATION FOR IMPROVED MULTI-CHANNEL SPEECH SEPARATION." Web.
1. . MULTI-BAND PIT AND MODEL INTEGRATION FOR IMPROVED MULTI-CHANNEL SPEECH SEPARATION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4141

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