3,039 research outputs found
Acoustic Data-driven Pronunciation Lexicon for Large Vocabulary Speech Recognition
Speech recognition systems normally use handcrafted pronunciation lexicons designed by linguistic experts. Building and maintaining such a lexicon is expensive and time consuming. This paper concerns automatically learning a pronunciation lexicon for speech recognition. We assume the availability of a small seed lexicon and then learn the pronunciations of new words directly from speech that is transcribed at word-level. We present two implementations for refining the putative pronunciations of new words based on acoustic evidence. The first one is an expectation maximization (EM) algorithm based on weighted finite state transducers (WFSTs) and the other is its Viterbi approximation. We carried out experiments on the Switchboard corpus of conversational telephone speech. The expert lexicon has a size of more than 30,000 words, from which we randomly selected 5,000 words to form the seed lexicon. By using the proposed lexicon learning method, we have significantly improved the accuracy compared with a lexicon learned using a grapheme-tophoneme transformation, and have obtained a word error rate that approaches that achieved using a fully handcrafted lexicon. Index Terms — Lexical modelling, Probabilistic pronunciation model, Automatic speech recognition
Acoustic data-driven lexicon learning based on a greedy pronunciation selection framework
Speech recognition systems for irregularly-spelled languages like English
normally require hand-written pronunciations. In this paper, we describe a
system for automatically obtaining pronunciations of words for which
pronunciations are not available, but for which transcribed data exists. Our
method integrates information from the letter sequence and from the acoustic
evidence. The novel aspect of the problem that we address is the problem of how
to prune entries from such a lexicon (since, empirically, lexicons with too
many entries do not tend to be good for ASR performance). Experiments on
various ASR tasks show that, with the proposed framework, starting with an
initial lexicon of several thousand words, we are able to learn a lexicon which
performs close to a full expert lexicon in terms of WER performance on test
data, and is better than lexicons built using G2P alone or with a pruning
criterion based on pronunciation probability
No Need for a Lexicon? Evaluating the Value of the Pronunciation Lexica in End-to-End Models
For decades, context-dependent phonemes have been the dominant sub-word unit
for conventional acoustic modeling systems. This status quo has begun to be
challenged recently by end-to-end models which seek to combine acoustic,
pronunciation, and language model components into a single neural network. Such
systems, which typically predict graphemes or words, simplify the recognition
process since they remove the need for a separate expert-curated pronunciation
lexicon to map from phoneme-based units to words. However, there has been
little previous work comparing phoneme-based versus grapheme-based sub-word
units in the end-to-end modeling framework, to determine whether the gains from
such approaches are primarily due to the new probabilistic model, or from the
joint learning of the various components with grapheme-based units.
In this work, we conduct detailed experiments which are aimed at quantifying
the value of phoneme-based pronunciation lexica in the context of end-to-end
models. We examine phoneme-based end-to-end models, which are contrasted
against grapheme-based ones on a large vocabulary English Voice-search task,
where we find that graphemes do indeed outperform phonemes. We also compare
grapheme and phoneme-based approaches on a multi-dialect English task, which
once again confirm the superiority of graphemes, greatly simplifying the system
for recognizing multiple dialects
A summary of the 2012 JHU CLSP Workshop on Zero Resource Speech Technologies and Models of Early Language Acquisition
We summarize the accomplishments of a multi-disciplinary workshop exploring the computational and scientific issues surrounding zero resource (unsupervised) speech technologies and related models of early language acquisition. Centered around the tasks of phonetic and lexical discovery, we consider unified evaluation metrics, present two new approaches for improving speaker independence in the absence of supervision, and evaluate the application of Bayesian word segmentation algorithms to automatic subword unit tokenizations. Finally, we present two strategies for integrating zero resource techniques into supervised settings, demonstrating the potential of unsupervised methods to improve mainstream technologies.5 page(s
Combined Acoustic and Pronunciation Modelling for Non-Native Speech Recognition
In this paper, we present several adaptation methods for non-native speech
recognition. We have tested pronunciation modelling, MLLR and MAP non-native
pronunciation adaptation and HMM models retraining on the HIWIRE foreign
accented English speech database. The ``phonetic confusion'' scheme we have
developed consists in associating to each spoken phone several sequences of
confused phones. In our experiments, we have used different combinations of
acoustic models representing the canonical and the foreign pronunciations:
spoken and native models, models adapted to the non-native accent with MAP and
MLLR. The joint use of pronunciation modelling and acoustic adaptation led to
further improvements in recognition accuracy. The best combination of the above
mentioned techniques resulted in a relative word error reduction ranging from
46% to 71%
Towards Language-Universal End-to-End Speech Recognition
Building speech recognizers in multiple languages typically involves
replicating a monolingual training recipe for each language, or utilizing a
multi-task learning approach where models for different languages have separate
output labels but share some internal parameters. In this work, we exploit
recent progress in end-to-end speech recognition to create a single
multilingual speech recognition system capable of recognizing any of the
languages seen in training. To do so, we propose the use of a universal
character set that is shared among all languages. We also create a
language-specific gating mechanism within the network that can modulate the
network's internal representations in a language-specific way. We evaluate our
proposed approach on the Microsoft Cortana task across three languages and show
that our system outperforms both the individual monolingual systems and systems
built with a multi-task learning approach. We also show that this model can be
used to initialize a monolingual speech recognizer, and can be used to create a
bilingual model for use in code-switching scenarios.Comment: submitted to ICASSP 201
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