3,655 research outputs found
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
Context-Dependent Acoustic Modeling without Explicit Phone Clustering
Phoneme-based acoustic modeling of large vocabulary automatic speech
recognition takes advantage of phoneme context. The large number of
context-dependent (CD) phonemes and their highly varying statistics require
tying or smoothing to enable robust training. Usually, Classification and
Regression Trees are used for phonetic clustering, which is standard in Hidden
Markov Model (HMM)-based systems. However, this solution introduces a secondary
training objective and does not allow for end-to-end training. In this work, we
address a direct phonetic context modeling for the hybrid Deep Neural Network
(DNN)/HMM, that does not build on any phone clustering algorithm for the
determination of the HMM state inventory. By performing different
decompositions of the joint probability of the center phoneme state and its
left and right contexts, we obtain a factorized network consisting of different
components, trained jointly. Moreover, the representation of the phonetic
context for the network relies on phoneme embeddings. The recognition accuracy
of our proposed models on the Switchboard task is comparable and outperforms
slightly the hybrid model using the standard state-tying decision trees.Comment: Submitted to Interspeech 202
Analyzing Hidden Representations in End-to-End Automatic Speech Recognition Systems
Neural models have become ubiquitous in automatic speech recognition systems.
While neural networks are typically used as acoustic models in more complex
systems, recent studies have explored end-to-end speech recognition systems
based on neural networks, which can be trained to directly predict text from
input acoustic features. Although such systems are conceptually elegant and
simpler than traditional systems, it is less obvious how to interpret the
trained models. In this work, we analyze the speech representations learned by
a deep end-to-end model that is based on convolutional and recurrent layers,
and trained with a connectionist temporal classification (CTC) loss. We use a
pre-trained model to generate frame-level features which are given to a
classifier that is trained on frame classification into phones. We evaluate
representations from different layers of the deep model and compare their
quality for predicting phone labels. Our experiments shed light on important
aspects of the end-to-end model such as layer depth, model complexity, and
other design choices.Comment: NIPS 201
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