Improving deep neural network acoustic modeling for audio corpus indexing under The IARPA Babel program

Xiaodong Cui; Brian Kingsbury; Jia Cui; Bhuvana Ramabhadran; Andrew Rosenberg; Mohammad Sadegh Rasooli; Owen Rambow; Nizar Habash; Vaibhava Goel

Improving deep neural network acoustic modeling for audio corpus indexing under The IARPA Babel program

Xiaodong Cui, Brian Kingsbury, Jia Cui, Bhuvana Ramabhadran, Andrew Rosenberg, Mohammad Sadegh Rasooli, Owen Rambow, Nizar Habash, Vaibhava Goel

Computer Science

Research output: Contribution to journal › Conference article › peer-review

Abstract

This paper is focused on several techniques that improve deep neural network (DNN) acoustic modeling for audio corpus indexing in the context of the IARPA Babel program. Specifically, fundamental frequency variation (FFV) and channelaware (CA) features and data augmentation based on stochastic feature mapping (SFM) are investigated not only for improved automatic speech recognition (ASR) performance but also for their impact to the final spoken term detection on the pre-indexed audio corpus. Experimental results on development languages of Babel option period one show that the improved DNN acoustic models can reduce word error rates in ASR and also help the keyword search performance compared to already competitive DNN baseline systems.

Original language	English (US)
Pages (from-to)	2103-2107
Number of pages	5
Journal	Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
State	Published - 2014
Event	15th Annual Conference of the International Speech Communication Association: Celebrating the Diversity of Spoken Languages, INTERSPEECH 2014 - Singapore, Singapore Duration: Sep 14 2014 → Sep 18 2014

Keywords

Channel-aware
Data augmentation
Deep neural networks
Fundamental frequency variation
Stochastic feature mapping

ASJC Scopus subject areas

Language and Linguistics
Human-Computer Interaction
Signal Processing
Software
Modeling and Simulation

Cite this

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title = "Improving deep neural network acoustic modeling for audio corpus indexing under The IARPA Babel program",

abstract = "This paper is focused on several techniques that improve deep neural network (DNN) acoustic modeling for audio corpus indexing in the context of the IARPA Babel program. Specifically, fundamental frequency variation (FFV) and channelaware (CA) features and data augmentation based on stochastic feature mapping (SFM) are investigated not only for improved automatic speech recognition (ASR) performance but also for their impact to the final spoken term detection on the pre-indexed audio corpus. Experimental results on development languages of Babel option period one show that the improved DNN acoustic models can reduce word error rates in ASR and also help the keyword search performance compared to already competitive DNN baseline systems.",

keywords = "Channel-aware, Data augmentation, Deep neural networks, Fundamental frequency variation, Stochastic feature mapping",

author = "Xiaodong Cui and Brian Kingsbury and Jia Cui and Bhuvana Ramabhadran and Andrew Rosenberg and Rasooli, {Mohammad Sadegh} and Owen Rambow and Nizar Habash and Vaibhava Goel",

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language = "English (US)",

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AU - Cui, Xiaodong

AU - Kingsbury, Brian

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AU - Ramabhadran, Bhuvana

AU - Rosenberg, Andrew

AU - Rasooli, Mohammad Sadegh

AU - Rambow, Owen

AU - Habash, Nizar

AU - Goel, Vaibhava

PY - 2014

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N2 - This paper is focused on several techniques that improve deep neural network (DNN) acoustic modeling for audio corpus indexing in the context of the IARPA Babel program. Specifically, fundamental frequency variation (FFV) and channelaware (CA) features and data augmentation based on stochastic feature mapping (SFM) are investigated not only for improved automatic speech recognition (ASR) performance but also for their impact to the final spoken term detection on the pre-indexed audio corpus. Experimental results on development languages of Babel option period one show that the improved DNN acoustic models can reduce word error rates in ASR and also help the keyword search performance compared to already competitive DNN baseline systems.

AB - This paper is focused on several techniques that improve deep neural network (DNN) acoustic modeling for audio corpus indexing in the context of the IARPA Babel program. Specifically, fundamental frequency variation (FFV) and channelaware (CA) features and data augmentation based on stochastic feature mapping (SFM) are investigated not only for improved automatic speech recognition (ASR) performance but also for their impact to the final spoken term detection on the pre-indexed audio corpus. Experimental results on development languages of Babel option period one show that the improved DNN acoustic models can reduce word error rates in ASR and also help the keyword search performance compared to already competitive DNN baseline systems.

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KW - Data augmentation

KW - Deep neural networks

KW - Fundamental frequency variation

KW - Stochastic feature mapping

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AN - SCOPUS:84910070307

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JF - Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH

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ER -

Improving deep neural network acoustic modeling for audio corpus indexing under The IARPA Babel program

Abstract

Keywords

ASJC Scopus subject areas

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