144 research outputs found
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
Low-rank and Sparse Soft Targets to Learn Better DNN Acoustic Models
Conventional deep neural networks (DNN) for speech acoustic modeling rely on
Gaussian mixture models (GMM) and hidden Markov model (HMM) to obtain binary
class labels as the targets for DNN training. Subword classes in speech
recognition systems correspond to context-dependent tied states or senones. The
present work addresses some limitations of GMM-HMM senone alignments for DNN
training. We hypothesize that the senone probabilities obtained from a DNN
trained with binary labels can provide more accurate targets to learn better
acoustic models. However, DNN outputs bear inaccuracies which are exhibited as
high dimensional unstructured noise, whereas the informative components are
structured and low-dimensional. We exploit principle component analysis (PCA)
and sparse coding to characterize the senone subspaces. Enhanced probabilities
obtained from low-rank and sparse reconstructions are used as soft-targets for
DNN acoustic modeling, that also enables training with untranscribed data.
Experiments conducted on AMI corpus shows 4.6% relative reduction in word error
rate
Phone-aware Neural Language Identification
Pure acoustic neural models, particularly the LSTM-RNN model, have shown
great potential in language identification (LID). However, the phonetic
information has been largely overlooked by most of existing neural LID models,
although this information has been used in the conventional phonetic LID
systems with a great success. We present a phone-aware neural LID architecture,
which is a deep LSTM-RNN LID system but accepts output from an RNN-based ASR
system. By utilizing the phonetic knowledge, the LID performance can be
significantly improved. Interestingly, even if the test language is not
involved in the ASR training, the phonetic knowledge still presents a large
contribution. Our experiments conducted on four languages within the Babel
corpus demonstrated that the phone-aware approach is highly effective.Comment: arXiv admin note: text overlap with arXiv:1705.0315
Phonetic Temporal Neural Model for Language Identification
Deep neural models, particularly the LSTM-RNN model, have shown great
potential for language identification (LID). However, the use of phonetic
information has been largely overlooked by most existing neural LID methods,
although this information has been used very successfully in conventional
phonetic LID systems. We present a phonetic temporal neural model for LID,
which is an LSTM-RNN LID system that accepts phonetic features produced by a
phone-discriminative DNN as the input, rather than raw acoustic features. This
new model is similar to traditional phonetic LID methods, but the phonetic
knowledge here is much richer: it is at the frame level and involves compacted
information of all phones. Our experiments conducted on the Babel database and
the AP16-OLR database demonstrate that the temporal phonetic neural approach is
very effective, and significantly outperforms existing acoustic neural models.
It also outperforms the conventional i-vector approach on short utterances and
in noisy conditions.Comment: Submitted to TASL
LID-senone Extraction via Deep Neural Networks for End-to-End Language Identification
A key problem in spoken language identification (LID) is how to effectively model features from a given speech utterance. Recent techniques such as end-to-end schemes and deep neural networks (DNNs) utilising transfer learning such as bottleneck (BN) features, have demonstrated good overall performance, but have not addressed the extraction of LID-specific features.
We thus propose a novel end-to-end neural network which aims to obtain effective LID-senone representations, which we define as being analogous to senones in speech recognition. We show that LID-senones combine a compact representation of the original acoustic feature space with a powerful descriptive and discriminative capability. Furthermore, a novel incremental training method is proposed to extract the weak language information buried in the acoustic features of insufficient language resources. Results on the six most confused languages in NIST LRE 2009 show good performance compared to state-of-the-art BN-GMM/i-vector and BN-DNN/i-vector systems. The proposed end-to-end network, coupled with an incremental training method which mitigates against over-fitting, has potential not just for LID, but also for other resource constrained tasks
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