57 research outputs found
Conversion of NNLM to Back-off language model in ASR
In daily life, automatic speech recognition is one of the aspect which is widely used for security system. To convert speech into text using neural network, Language model is one of the block on which efficiency of speech recognition depends. In this paper we developed an algorithm to convert Neural Network Language model (NNLM) to Back-off language model for more efficient decoding. For large vocabulary system this conversion gives more efficient result. Efficiency of language model depends on perplexity and Word Error Rate (WER
Server-side Rescoring of Spoken Entity-centric Knowledge Queries for Virtual Assistants
On-device Virtual Assistants (VAs) powered by Automatic Speech Recognition
(ASR) require effective knowledge integration for the challenging entity-rich
query recognition. In this paper, we conduct an empirical study of modeling
strategies for server-side rescoring of spoken information domain queries using
various categories of Language Models (LMs) (N-gram word LMs, sub-word neural
LMs). We investigate the combination of on-device and server-side signals, and
demonstrate significant WER improvements of 23%-35% on various entity-centric
query subpopulations by integrating various server-side LMs compared to
performing ASR on-device only. We also perform a comparison between LMs trained
on domain data and a GPT-3 variant offered by OpenAI as a baseline.
Furthermore, we also show that model fusion of multiple server-side LMs trained
from scratch most effectively combines complementary strengths of each model
and integrates knowledge learned from domain-specific data to a VA ASR system
Efficient Embedded Decoding of Neural Network Language Models in a Machine Translation System
[EN] Neural Network Language Models (NNLMs) are a successful approach to Natural Language Processing tasks, such as Machine Translation. We introduce in this work a Statistical Machine Translation (SMT) system which fully integrates NNLMs in the decoding stage, breaking the traditional approach based on n-best list rescoring. The neural net models (both language models (LMs) and translation models) are fully coupled in the decoding stage, allowing to more strongly influence the translation quality. Computational issues were solved by using a novel idea based on memorization and smoothing of the softmax constants to avoid their computation, which introduces a trade-off between LM quality and computational cost. These ideas were studied in a machine translation task with different combinations of neural networks used both as translation models and as target LMs, comparing phrase-based and N-gram-based systems, showing that the integrated approach seems more promising for N-gram-based systems, even with nonfull-quality NNLMs.This work was partially supported by the Spanish MINECO and FEDER found under project TIN2017-85854-C4-2-R.Zamora Martínez, FJ.; Castro-Bleda, MJ. (2018). Efficient Embedded Decoding of Neural Network Language Models in a Machine Translation System. International Journal of Neural Systems. 28(9). https://doi.org/10.1142/S0129065718500077S28
Morphologically motivated word classes for very large vocabulary speech recognition of Finnish and Estonian
We study class-based n-gram and neural network language models for very large vocabulary speech recognition of two morphologically rich languages: Finnish and Estonian. Due to morphological processes such as derivation, inflection and compounding, the models need to be trained with vocabulary sizes of several millions of word types. Class-based language modelling is in this case a powerful approach to alleviate the data sparsity and reduce the computational load. For a very large vocabulary, bigram statistics may not be an optimal way to derive the classes. We thus study utilizing the output of a morphological analyzer to achieve efficient word classes. We show that efficient classes can be learned by refining the morphological classes to smaller equivalence classes using merging, splitting and exchange procedures with suitable constraints. This type of classification can improve the results, particularly when language model training data is not very large. We also extend the previous analyses by rescoring the hypotheses obtained from a very large vocabulary recognizer using class-based neural network language models. We show that despite the fixed vocabulary, carefully constructed classes for word-based language models can in some cases result in lower error rates than subword-based unlimited vocabulary language models.We study class-based n-gram and neural network language models for very large vocabulary speech recognition of two morphologically rich languages: Finnish and Estonian. Due to morphological processes such as derivation, inflection and compounding, the models need to be trained with vocabulary sizes of several millions of word types. Class-based language modelling is in this case a powerful approach to alleviate the data sparsity and reduce the computational load. For a very large vocabulary, bigram statistics may not be an optimal way to derive the classes. We thus study utilizing the output of a morphological analyzer to achieve efficient word classes. We show that efficient classes can be learned by refining the morphological classes to smaller equivalence classes using merging, splitting and exchange procedures with suitable constraints. This type of classification can improve the results, particularly when language model training data is not very large. We also extend the previous analyses by rescoring the hypotheses obtained from a very large vocabulary recognizer using class-based neural network language models. We show that despite the fixed vocabulary, carefully constructed classes for word-based language models can in some cases result in lower error rates than subword-based unlimited vocabulary language models.Peer reviewe
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Efficient lattice rescoring using recurrent neural network language models
This is the accepted manuscript of a paper published in the 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on, Issue Date: 4-9 May 2014, Written by: Liu, X.; Wang, Y.; Chen, X.; Gales, M.J.F.; Woodland, P.C.).Recurrent neural network language models (RNNLM) have become
an increasingly popular choice for state-of-the-art speech recognition
systems due to their inherently strong generalization performance.
As these models use a vector representation of complete
history contexts, RNNLMs are normally used to rescore N-best lists.
Motivated by their intrinsic characteristics, two novel lattice rescoring
methods for RNNLMs are investigated in this paper. The first
uses an n-gram style clustering of history contexts. The second approach
directly exploits the distance measure between hidden history
vectors. Both methods produced 1-best performance comparable
with a 10k-best rescoring baseline RNNLMsystem on a large vocabulary
conversational telephone speech recognition task. Significant
lattice size compression of over 70% and consistent improvements
after confusion network (CN) decoding were also obtained over the
N-best rescoring approach.The research leading to these results was supported by EPSRC grant
EP/I031022/1 (Natural Speech Technology) and DARPA under the Broad
Operational Language Translation (BOLT) and RATS programs
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Two efficient lattice rescoring methods using recurrent neural network language models
An important part of the language modelling problem for automatic speech recognition (ASR) systems, and many other related applications, is to appropriately model long-distance context dependencies in natural languages. Hence, statistical language models (LMs) that can model longer span history contexts, for example, recurrent neural network language models (RNNLMs), have become increasingly popular for state-of-the-art ASR systems. As RNNLMs use a vector representation of complete history contexts, they are normally used to rescore N-best lists. Motivated by their intrinsic characteristics, two efficient lattice rescoring methods for RNNLMs are proposed in this paper. The first method uses an -gram style clustering of history contexts. The second approach directly exploits the distance measure between recurrent hidden history vectors. Both methods produced 1-best performance comparable to a 10 k-best rescoring baseline RNNLM system on two large vocabulary conversational telephone speech recognition tasks for US English and Mandarin Chinese. Consistent lattice size compression and recognition performance improvements after confusion network (CN) decoding were also obtained over the prefix tree structured N-best rescoring approach.This work was supported by EPSRC under Grant EP/I031022/1 (Natural Speech Technology) and DARPA under the Broad Operational Language Translation and RATS programs. The work of X. Chen was supported by Toshiba Research Europe Ltd, Cambridge Research Lab.This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/TASLP.2016.255882
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Paraphrastic language models and combination with neural network language models
In natural languages multiple word sequences can represent the same
underlying meaning. Only modelling the observed surface word sequence can result in poor context coverage, for example, when using n-gram language models (LM). To handle this issue, paraphrastic LMs were proposed in previous research and successfully applied to a US English conversational telephone speech transcription
task. In order to exploit the complementary characteristics of paraphrastic LMs and neural network LMs (NNLM), the combination
between the two is investigated in this paper. To investigate paraphrastic LMs’ generalization ability to other languages, experiments
are conducted on a Mandarin Chinese broadcast speech transcription task. Using a paraphrastic multi-level LM modelling both word
and phrase sequences, significant error rate reductions of 0.9% absolute (9% relative) and 0.5% absolute (5% relative) were obtained
over the baseline n-gram and NNLM systems respectively, after a
combination with word and phrase level NNLMs.The research leading to these results was supported by EPSRC Programme Grant EP/I031022/1 (Natural Speech Technology)This is the author accepted manuscript. The final version is available at http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6639308
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