297 research outputs found
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
LiCo-Net: Linearized Convolution Network for Hardware-efficient Keyword Spotting
This paper proposes a hardware-efficient architecture, Linearized Convolution
Network (LiCo-Net) for keyword spotting. It is optimized specifically for
low-power processor units like microcontrollers. ML operators exhibit
heterogeneous efficiency profiles on power-efficient hardware. Given the exact
theoretical computation cost, int8 operators are more computation-effective
than float operators, and linear layers are often more efficient than other
layers. The proposed LiCo-Net is a dual-phase system that uses the efficient
int8 linear operators at the inference phase and applies streaming convolutions
at the training phase to maintain a high model capacity. The experimental
results show that LiCo-Net outperforms single-value decomposition filter (SVDF)
on hardware efficiency with on-par detection performance. Compared to SVDF,
LiCo-Net reduces cycles by 40% on HiFi4 DSP
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
HEiMDaL: Highly Efficient Method for Detection and Localization of wake-words
Streaming keyword spotting is a widely used solution for activating voice
assistants. Deep Neural Networks with Hidden Markov Model (DNN-HMM) based
methods have proven to be efficient and widely adopted in this space, primarily
because of the ability to detect and identify the start and end of the wake-up
word at low compute cost. However, such hybrid systems suffer from loss metric
mismatch when the DNN and HMM are trained independently. Sequence
discriminative training cannot fully mitigate the loss-metric mismatch due to
the inherent Markovian style of the operation. We propose an low footprint CNN
model, called HEiMDaL, to detect and localize keywords in streaming conditions.
We introduce an alignment-based classification loss to detect the occurrence of
the keyword along with an offset loss to predict the start of the keyword.
HEiMDaL shows 73% reduction in detection metrics along with equivalent
localization accuracy and with the same memory footprint as existing DNN-HMM
style models for a given wake-word
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