1,714 research outputs found
Optimizing expected word error rate via sampling for speech recognition
State-level minimum Bayes risk (sMBR) training has become the de facto
standard for sequence-level training of speech recognition acoustic models. It
has an elegant formulation using the expectation semiring, and gives large
improvements in word error rate (WER) over models trained solely using
cross-entropy (CE) or connectionist temporal classification (CTC). sMBR
training optimizes the expected number of frames at which the reference and
hypothesized acoustic states differ. It may be preferable to optimize the
expected WER, but WER does not interact well with the expectation semiring, and
previous approaches based on computing expected WER exactly involve expanding
the lattices used during training. In this paper we show how to perform
optimization of the expected WER by sampling paths from the lattices used
during conventional sMBR training. The gradient of the expected WER is itself
an expectation, and so may be approximated using Monte Carlo sampling. We show
experimentally that optimizing WER during acoustic model training gives 5%
relative improvement in WER over a well-tuned sMBR baseline on a 2-channel
query recognition task (Google Home)
Open-vocabulary spoken utterance retrieval using confusion networks
This paper presents a novel approach to open-vocabulary spoken utterance retrieval using confusion networks. If out-of-vocabulary (OOV) words are present in queries and the corpus, word-based indexing will not be sufficient. For this problem, we apply phone confusion networks and combine them with word confusion networks. With this approach, we can generate a more compact index table that enables robust keyword matching compared with typical lattice-based methods. In the retrieval experiments with speech recordings in MIT lecture corpus, our method using phone confusion networks outperformed lattice-based methods especially for OOV queries
Tagging and parsing with cascaded Markov models : automation of corpus annotation
This thesis presents new techniques for parsing natural language. They are based on Markov Models, which are commonly used in part-of-speech tagging for sequential processing on the world level. We show that Markov Models can be successfully applied to other levels of syntactic processing. first two classification task are handled: the assignment of grammatical functions and the labeling of non-terminal nodes. Then, Markov Models are used to recognize hierarchical syntactic structures. Each layer of a structure is represented by a separate Markov Model. The output of a lower layer is passed as input to a higher layer, hence the name: Cascaded Markov Models. Instead of simple symbols, the states emit partial context-free structures. The new techniques are applied to corpus annotation and partial parsing and are evaluated using corpora of different languages and domains.Ausgehend von Markov-Modellen, die für das Part-of-Speech-Tagging eingesetzt werden, stellt diese Arbeit Verfahren vor, die Markov-Modelle auch auf weiteren Ebenen der syntaktischen Verarbeitung erfolgreich nutzen. Dies betrifft zum einen Klassifikationen wie die Zuweisung grammatischer Funktionen und die Bestimmung von Kategorien nichtterminaler Knoten, zum anderen die Zuweisung hierarchischer, syntaktischer Strukturen durch Markov-Modelle. Letzteres geschieht durch die Repräsentation jeder Ebene einer syntaktischen Struktur durch ein eigenes Markov-Modell, was den Namen des Verfahrens prägt: Kaskadierte Markov-Modelle. Deren Zustände geben anstelle atomarer Symbole partielle kontextfreie Strukturen aus. Diese Verfahren kommen in der Korpusannotation und dem partiellen Parsing zum Einsatz und werden anhand mehrerer Korpora evaluiert
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The Roles of Language Models and Hierarchical Models in Neural Sequence-to-Sequence Prediction
With the advent of deep learning, research in many areas of machine learning is converging towards the same set of methods and models. For example, long short-term memory networks are not only popular for various tasks in natural language processing (NLP) such as speech recognition, machine translation, handwriting recognition, syntactic parsing, etc., but they are also applicable to seemingly unrelated fields such as robot control, time series prediction, and bioinformatics. Recent advances in contextual word embeddings like BERT boast with achieving state-of-the-art results on 11 NLP tasks with the same model. Before deep learning, a speech recognizer and a syntactic parser used to have little in common as systems were much more tailored towards the task at hand.
At the core of this development is the tendency to view each task as yet another data mapping problem, neglecting the particular characteristics and (soft) requirements tasks often have in practice. This often goes along with a sharp break of deep learning methods with previous research in the specific area. This work can be understood as an antithesis to this paradigm. We show how traditional symbolic statistical machine translation models can still improve neural machine translation (NMT) while reducing the risk for common pathologies of NMT such as hallucinations and neologisms. Other external symbolic models such as spell checkers and morphology databases help neural grammatical error correction. We also focus on language models that often do not play a role in vanilla end-to-end approaches and apply them in different ways to word reordering, grammatical error correction, low-resource NMT, and document-level NMT. Finally, we demonstrate the benefit of hierarchical models in sequence-to-sequence prediction. Hand-engineered covering grammars are effective in preventing catastrophic errors in neural text normalization systems. Our operation sequence model for interpretable NMT represents translation as a series of actions that modify the translation state, and can also be seen as derivation in a formal grammar.EPSRC grant EP/L027623/1
EPSRC Tier-2 capital grant EP/P020259/
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