9,734 research outputs found
Comparing Human and Machine Errors in Conversational Speech Transcription
Recent work in automatic recognition of conversational telephone speech (CTS)
has achieved accuracy levels comparable to human transcribers, although there
is some debate how to precisely quantify human performance on this task, using
the NIST 2000 CTS evaluation set. This raises the question what systematic
differences, if any, may be found differentiating human from machine
transcription errors. In this paper we approach this question by comparing the
output of our most accurate CTS recognition system to that of a standard speech
transcription vendor pipeline. We find that the most frequent substitution,
deletion and insertion error types of both outputs show a high degree of
overlap. The only notable exception is that the automatic recognizer tends to
confuse filled pauses ("uh") and backchannel acknowledgments ("uhhuh"). Humans
tend not to make this error, presumably due to the distinctive and opposing
pragmatic functions attached to these words. Furthermore, we quantify the
correlation between human and machine errors at the speaker level, and
investigate the effect of speaker overlap between training and test data.
Finally, we report on an informal "Turing test" asking humans to discriminate
between automatic and human transcription error cases
The Microsoft 2017 Conversational Speech Recognition System
We describe the 2017 version of Microsoft's conversational speech recognition
system, in which we update our 2016 system with recent developments in
neural-network-based acoustic and language modeling to further advance the
state of the art on the Switchboard speech recognition task. The system adds a
CNN-BLSTM acoustic model to the set of model architectures we combined
previously, and includes character-based and dialog session aware LSTM language
models in rescoring. For system combination we adopt a two-stage approach,
whereby subsets of acoustic models are first combined at the senone/frame
level, followed by a word-level voting via confusion networks. We also added a
confusion network rescoring step after system combination. The resulting system
yields a 5.1\% word error rate on the 2000 Switchboard evaluation set
English Conversational Telephone Speech Recognition by Humans and Machines
One of the most difficult speech recognition tasks is accurate recognition of
human to human communication. Advances in deep learning over the last few years
have produced major speech recognition improvements on the representative
Switchboard conversational corpus. Word error rates that just a few years ago
were 14% have dropped to 8.0%, then 6.6% and most recently 5.8%, and are now
believed to be within striking range of human performance. This then raises two
issues - what IS human performance, and how far down can we still drive speech
recognition error rates? A recent paper by Microsoft suggests that we have
already achieved human performance. In trying to verify this statement, we
performed an independent set of human performance measurements on two
conversational tasks and found that human performance may be considerably
better than what was earlier reported, giving the community a significantly
harder goal to achieve. We also report on our own efforts in this area,
presenting a set of acoustic and language modeling techniques that lowered the
word error rate of our own English conversational telephone LVCSR system to the
level of 5.5%/10.3% on the Switchboard/CallHome subsets of the Hub5 2000
evaluation, which - at least at the writing of this paper - is a new
performance milestone (albeit not at what we measure to be human performance!).
On the acoustic side, we use a score fusion of three models: one LSTM with
multiple feature inputs, a second LSTM trained with speaker-adversarial
multi-task learning and a third residual net (ResNet) with 25 convolutional
layers and time-dilated convolutions. On the language modeling side, we use
word and character LSTMs and convolutional WaveNet-style language models
Practical Hidden Voice Attacks against Speech and Speaker Recognition Systems
Voice Processing Systems (VPSes), now widely deployed, have been made
significantly more accurate through the application of recent advances in
machine learning. However, adversarial machine learning has similarly advanced
and has been used to demonstrate that VPSes are vulnerable to the injection of
hidden commands - audio obscured by noise that is correctly recognized by a VPS
but not by human beings. Such attacks, though, are often highly dependent on
white-box knowledge of a specific machine learning model and limited to
specific microphones and speakers, making their use across different acoustic
hardware platforms (and thus their practicality) limited. In this paper, we
break these dependencies and make hidden command attacks more practical through
model-agnostic (blackbox) attacks, which exploit knowledge of the signal
processing algorithms commonly used by VPSes to generate the data fed into
machine learning systems. Specifically, we exploit the fact that multiple
source audio samples have similar feature vectors when transformed by acoustic
feature extraction algorithms (e.g., FFTs). We develop four classes of
perturbations that create unintelligible audio and test them against 12 machine
learning models, including 7 proprietary models (e.g., Google Speech API, Bing
Speech API, IBM Speech API, Azure Speaker API, etc), and demonstrate successful
attacks against all targets. Moreover, we successfully use our maliciously
generated audio samples in multiple hardware configurations, demonstrating
effectiveness across both models and real systems. In so doing, we demonstrate
that domain-specific knowledge of audio signal processing represents a
practical means of generating successful hidden voice command attacks
Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments
Eliminating the negative effect of non-stationary environmental noise is a
long-standing research topic for automatic speech recognition that stills
remains an important challenge. Data-driven supervised approaches, including
ones based on deep neural networks, have recently emerged as potential
alternatives to traditional unsupervised approaches and with sufficient
training, can alleviate the shortcomings of the unsupervised methods in various
real-life acoustic environments. In this light, we review recently developed,
representative deep learning approaches for tackling non-stationary additive
and convolutional degradation of speech with the aim of providing guidelines
for those involved in the development of environmentally robust speech
recognition systems. We separately discuss single- and multi-channel techniques
developed for the front-end and back-end of speech recognition systems, as well
as joint front-end and back-end training frameworks
- …