Leif Johnson
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A more general method for pronunciation learning
Antoine Bruguier
Interspeech 2017 (2017)
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Automatic speech recognition relies on pronunciation dictionaries for accurate results and previous work used pronunciation learning algorithms to build them. Efficient algorithms must balance having the ability to learn varied pronunciations while being constrained enough to be robust. Our approach extends one of such algorithms \cite{Kou2015} by replacing a finite state transducer (FST) built from a limited-size candidate list with a general and flexible FST building mechanism. This architecture can accommodate a wide variety of pronunciation predictions and can also learn pronunciations without having the written form. It can also use an FST built from a recursive neural network (RNN) and tune the importance given to the written form. The new approach reduces the number of incorrect pronunciations learned by up to 25% (relative) on a random sampling of Google voice traffic
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A Comparison of Sequence-to-Sequence Models for Speech Recognition
Navdeep Jaitly
Interspeech 2017, ISCA (2017)
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In this work, we conduct a detailed evaluation of various all-neural, end-to-end trained, sequence-to-sequence models applied to the task of speech recognition. Notably, each of these systems directly predicts graphemes in the written domain, without using an external pronunciation lexicon, or a separate language model. We examine several sequence-to-sequence models including connectionist temporal classification (CTC), the recurrent neural network (RNN) transducer, an attention-based model, and a model which augments the RNN-transducer with an attention mechanism.
We find that end-to-end models are capable of learning all components of the speech recognition process: acoustic, pronunciation, and language models, directly outputting words in the written form (e.g., “one hundred dollars” to “$100”), in a single jointly-optimized neural network. Furthermore, the sequence-to-sequence models are competitive with traditional state-of-the-art approaches on dictation test sets, although the baseline outperforms these models on voice-search test sets.
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Distributed Discriminative Language Models for Google Voice Search
Preethi Jyothi
Brian Strope
Proceedings of ICASSP 2012, IEEE, pp. 5017-5021
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This paper considers large-scale linear discriminative language models trained using a distributed perceptron algorithm. The algorithm is implemented efficiently using a
MapReduce/SSTable framework. This work also introduces the use of large amounts of unsupervised data (confidence filtered Google voice-search logs) in conjunction with a novel training procedure that regenerates word lattices for the given data with a weaker acoustic model than the one used to generate the unsupervised transcriptions for the logged data. We observe small but statistically significant improvements in recognition performance after reranking N-best lists of a standard Google voice-search data set.
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Language Modeling for Automatic Speech Recognition Meets the Web: Google Search by Voice
Johan Schalkwyk
Boulos Harb
Peng Xu
Preethi Jyothi
Thorsten Brants
Vida Ha
Will Neveitt
University of Toronto (2012)
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A critical component of a speech recognition system targeting web search is the language model. The talk presents an empirical exploration of the google.com query stream with the end goal of high quality statistical language modeling for mobile voice search. Our experiments show that after text normalization the query
stream is not as ``wild'' as it seems at first sight. One can achieve out-of-vocabulary rates below 1% using a one million word vocabulary, and excellent n-gram hit ratios of 77/88% even at high orders such as n=5/4, respectively.
Using large scale, distributed language models can improve performance significantly---up to 10\% relative reductions in word-error-rate over conventional models used in speech recognition. We also find that the query stream is non-stationary, which means that adding more past training data beyond a certain point provides diminishing returns, and may even degrade performance slightly. Perhaps less surprisingly, we have shown that locale matters significantly for English query data across USA, Great Britain and Australia. In an attempt to leverage the speech data in voice search logs, we successfully build large-scale discriminative N-gram language models and derive small but significant gains in recognition performance.
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Large-scale Discriminative Language Model Reranking for Voice Search
Preethi Jyothi
Brian Strope
Proceedings of the NAACL-HLT 2012 Workshop: Will We Ever Really Replace the N-gram Model? On the Future of Language Modeling for HLT, Association for Computational Linguistics, pp. 41-49
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We present a distributed framework for large-scale discriminative language models that can be integrated within a large vocabulary continuous speech recognition (LVCSR) system using lattice rescoring. We intentionally use a weakened acoustic model in a baseline LVCSR system to generate candidate hypotheses for voice-search data; this allows us to utilize large amounts of unsupervised data to train our models. We propose an efficient and scalable MapReduce framework that uses a perceptron-style distributed training strategy to handle these large amounts of data. We report small but significant improvements in recognition accuracies on a standard voice-search data set using our discriminative reranking model. We also provide an analysis of the various parameters of our models including model size, types of features, size of partitions in the MapReduce framework with the help of supporting experiments.
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