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    <titleInfo>
        <title>Parameter optimization for iterative confusion network decoding in weather-domain speech recognition</title>
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    <name type="personal">
        <namePart type="given">Shahab</namePart>
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        <dateIssued>2013-dec 5-6</dateIssued>
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            <title>Proceedings of the 10th International Workshop on Spoken Language Translation: Papers</title>
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            <namePart type="given">Joy</namePart>
            <namePart type="given">Ying</namePart>
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    <abstract>In this paper, we apply a set of approaches to, efficiently, rescore the output of the automatic speech recognition over weather-domain data. Since the in-domain data is usually insufficient for training an accurate language model (LM) we utilize an automatic selection method to extract domain-related sentences from a general text resource. Then, an N-gram language model is trained on this set. We exploit this LM, along with a pre-trained acoustic model for recognition of the development and test instances. The recognizer generates a confusion network (CN) for each instance. Afterwards, we make use of the recurrent neural network language model (RNNLM), trained on the in-domain data, in order to iteratively rescore the CNs. Rescoring the CNs, in this way, requires estimating the weights of the RNNLM, N-gramLM and acoustic model scores. Weights optimization is the critical part of this work, whereby, we propose using the minimum error rate training (MERT) algorithm along with a novel N-best list extraction method. The experiments are done over weather forecast domain data that has been provided in the framework of EUBRIDGE project.</abstract>
    <identifier type="citekey">jalalvand-falavigna-2013-parameter</identifier>
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        <url>https://aclanthology.org/2013.iwslt-papers.19</url>
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%0 Conference Proceedings
%T Parameter optimization for iterative confusion network decoding in weather-domain speech recognition
%A Jalalvand, Shahab
%A Falavigna, Daniele
%Y Zhang, Joy Ying
%S Proceedings of the 10th International Workshop on Spoken Language Translation: Papers
%D 2013
%8 dec 5 6
%C Heidelberg, Germany
%F jalalvand-falavigna-2013-parameter
%X In this paper, we apply a set of approaches to, efficiently, rescore the output of the automatic speech recognition over weather-domain data. Since the in-domain data is usually insufficient for training an accurate language model (LM) we utilize an automatic selection method to extract domain-related sentences from a general text resource. Then, an N-gram language model is trained on this set. We exploit this LM, along with a pre-trained acoustic model for recognition of the development and test instances. The recognizer generates a confusion network (CN) for each instance. Afterwards, we make use of the recurrent neural network language model (RNNLM), trained on the in-domain data, in order to iteratively rescore the CNs. Rescoring the CNs, in this way, requires estimating the weights of the RNNLM, N-gramLM and acoustic model scores. Weights optimization is the critical part of this work, whereby, we propose using the minimum error rate training (MERT) algorithm along with a novel N-best list extraction method. The experiments are done over weather forecast domain data that has been provided in the framework of EUBRIDGE project.
%U https://aclanthology.org/2013.iwslt-papers.19

Markdown (Informal)

[Parameter optimization for iterative confusion network decoding in weather-domain speech recognition](https://aclanthology.org/2013.iwslt-papers.19) (Jalalvand & Falavigna, IWSLT 2013)

• Parameter optimization for iterative confusion network decoding in weather-domain speech recognition (Jalalvand & Falavigna, IWSLT 2013)

ACL

• Shahab Jalalvand and Daniele Falavigna. 2013. Parameter optimization for iterative confusion network decoding in weather-domain speech recognition. In Proceedings of the 10th International Workshop on Spoken Language Translation: Papers, Heidelberg, Germany.