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Operational assessment of keyword search on oral history

May 23, 2016
  |
Conference Paper
Author:
Elizabeth E. Salesky
…
Published in:
10th Language Resources and Evaluation Conf., LREC 2016, 23-8 May 2016.
Topic:
human language technology
R&D area:
R&D group:

Summary

This project assesses the resources necessary to make oral history searchable by means of automatic speech recognition (ASR). There are many inherent challenges in applying ASR to conversational speech: smaller training set sizes and varying demographics, among others. We assess the impact of dataset size, word error rate and term-weighted value on human search capability through an information retrieval task on Mechanical Turk. We use English oral history data collected by StoryCorps, a national organization that provides all people with the opportunity to record, share and preserve their stories, and control for a variety of demographics including age, gender, birthplace, and dialect on four different training set sizes. We show comparable search performance using a standard speech recognition system as with hand-transcribed data, which is promising for increased accessibility of conversational speech and oral history archives.
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Summary

This project assesses the resources necessary to make oral history searchable by means of automatic speech recognition (ASR). There are many inherent challenges in applying ASR to conversational speech: smaller training set sizes and varying demographics, among others. We assess the impact of dataset size, word error rate and term-weighted...

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Operational assessment of keyword search on oral history

Analysis of factors affecting system performance in the ASpIRE challenge

December 13, 2015
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Conference Paper
Author:
Jennifer T. Melot
…
Published in:
2015 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2015, 13-17 December 2015.
Topic:
human language technology
R&D area:
R&D group:

Summary

This paper presents an analysis of factors affecting system performance in the ASpIRE (Automatic Speech recognition In Reverberant Environments) challenge. In particular, overall word error rate (WER) of the solver systems is analyzed as a function of room, distance between talker and microphone, and microphone type. We also analyze speech activity detection performance of the solver systems and investigate its relationship to WER. The primary goal of the paper is to provide insight into the factors affecting system performance in the ASpIRE evaluation set across many systems given annotations and metadata that are not available to the solvers. This analysis will inform the design of future challenges and provide insight into the efficacy of current solutions addressing noisy reverberant speech in mismatched conditions.
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Summary

This paper presents an analysis of factors affecting system performance in the ASpIRE (Automatic Speech recognition In Reverberant Environments) challenge. In particular, overall word error rate (WER) of the solver systems is analyzed as a function of room, distance between talker and microphone, and microphone type. We also analyze speech...

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Analysis of factors affecting system performance in the ASpIRE challenge

The MITLL/AFRL IWSLT-2014 MT System

December 4, 2014
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Conference Paper
Author:
Michaeel M. Kazi
…
Published in:
Proc. 11th Int. Workshop on Spoken Language Translation, IWSLT, 4-5 December 2014.
Topic:
human language technology
R&D area:
R&D group:

Summary

This report summarizes the MITLL-AFRL MT and ASR systems and the experiments run using them during the 2014 IWSLT evaluation campaign. Our MT system is much improved over last year, owing to integration of techniques such as PRO and DREM optimization, factored language models, neural network joint model rescoring, multiple phrase tables, and development set creation. We focused our efforts this year on the tasks of translating from Arabic, Russian, Chinese, and Farsi into English, as well as translating from English to French. ASR performance also improved, partly due to increased efforts with deep neural networks for hybrid and tandem systems. Work focused on both the English and Italian ASR tasks.
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Summary

This report summarizes the MITLL-AFRL MT and ASR systems and the experiments run using them during the 2014 IWSLT evaluation campaign. Our MT system is much improved over last year, owing to integration of techniques such as PRO and DREM optimization, factored language models, neural network joint model rescoring, multiple...

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The MITLL/AFRL IWSLT-2014 MT System

Comparing a high and low-level deep neural network implementation for automatic speech recognition

November 17, 2014
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Conference Paper
Author:
Jessica M. Ray
…
Published in:
1st Workshop for High Performance Technical Computing in Dynamic Languages, HPTCDL 2014, 17 November 2014.
Topic:
speech recognition
R&D area:
R&D group:

Summary

The use of deep neural networks (DNNs) has improved performance in several fields including computer vision, natural language processing, and automatic speech recognition (ASR). The increased use of DNNs in recent years has been largely due to performance afforded by GPUs, as the computational cost of training large networks on a CPU is prohibitive. Many training algorithms are well-suited to the GPU; however, writing hand-optimized GPGPU code is a significant undertaking. More recently, high-level libraries have attempted to simplify GPGPU development by automatically performing tasks such as optimization and code generation. This work utilizes Theano, a high-level Python library, to implement a DNN for the purpose of phone recognition in ASR. Performance is compared against a low-level, hand-optimized C++/CUDA DNN implementation from Kaldi, a popular ASR toolkit. Results show that the DNN implementation in Theano has CPU and GPU runtimes on par with that of Kaldi, while requiring approximately 95% less lines of code.
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Summary

The use of deep neural networks (DNNs) has improved performance in several fields including computer vision, natural language processing, and automatic speech recognition (ASR). The increased use of DNNs in recent years has been largely due to performance afforded by GPUs, as the computational cost of training large networks on...

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Comparing a high and low-level deep neural network implementation for automatic speech recognition

The MIT-LL/AFRL IWSLT-2013 MT System

December 5, 2013
  |
Conference Paper
Author:
Michaeel M. Kazi
…
Published in:
Proc. 10th Int. Workshop on Spoken Language Translation, IWSLT, 5-6 December 2013.
Topic:
human language technology
R&D area:
R&D group:

Summary

This paper describes the MIT-LL/AFRL statistical MT system and the improvements that were developed during the IWSLT 2013 evaluation campaign [1]. As part of these efforts, we experimented with a number of extensions to the standard phrase-based model that improve performance on the Russian to English, Chinese to English, Arabic to English, and English to French TED-talk translation task. We also applied our existing ASR system to the TED-talk lecture ASR task. We discuss the architecture of the MIT-LL/AFRL MT system, improvements over our 2012 system, and experiments we ran during the IWSLT-2013 evaluation. Specifically, we focus on 1) cross-entropy filtering of MT training data, and 2) improved optimization techniques, 3) language modeling, and 4) approximation of out-of-vocabulary words.
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Summary

This paper describes the MIT-LL/AFRL statistical MT system and the improvements that were developed during the IWSLT 2013 evaluation campaign [1]. As part of these efforts, we experimented with a number of extensions to the standard phrase-based model that improve performance on the Russian to English, Chinese to English, Arabic...

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The MIT-LL/AFRL IWSLT-2013 MT System
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