2,697 research outputs found
Visually grounded learning of keyword prediction from untranscribed speech
During language acquisition, infants have the benefit of visual cues to
ground spoken language. Robots similarly have access to audio and visual
sensors. Recent work has shown that images and spoken captions can be mapped
into a meaningful common space, allowing images to be retrieved using speech
and vice versa. In this setting of images paired with untranscribed spoken
captions, we consider whether computer vision systems can be used to obtain
textual labels for the speech. Concretely, we use an image-to-words multi-label
visual classifier to tag images with soft textual labels, and then train a
neural network to map from the speech to these soft targets. We show that the
resulting speech system is able to predict which words occur in an
utterance---acting as a spoken bag-of-words classifier---without seeing any
parallel speech and text. We find that the model often confuses semantically
related words, e.g. "man" and "person", making it even more effective as a
semantic keyword spotter.Comment: 5 pages, 3 figures, 5 tables; small updates, added link to code;
accepted to Interspeech 201
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
Deep Spoken Keyword Spotting:An Overview
Spoken keyword spotting (KWS) deals with the identification of keywords in
audio streams and has become a fast-growing technology thanks to the paradigm
shift introduced by deep learning a few years ago. This has allowed the rapid
embedding of deep KWS in a myriad of small electronic devices with different
purposes like the activation of voice assistants. Prospects suggest a sustained
growth in terms of social use of this technology. Thus, it is not surprising
that deep KWS has become a hot research topic among speech scientists, who
constantly look for KWS performance improvement and computational complexity
reduction. This context motivates this paper, in which we conduct a literature
review into deep spoken KWS to assist practitioners and researchers who are
interested in this technology. Specifically, this overview has a comprehensive
nature by covering a thorough analysis of deep KWS systems (which includes
speech features, acoustic modeling and posterior handling), robustness methods,
applications, datasets, evaluation metrics, performance of deep KWS systems and
audio-visual KWS. The analysis performed in this paper allows us to identify a
number of directions for future research, including directions adopted from
automatic speech recognition research and directions that are unique to the
problem of spoken KWS
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