109 research outputs found
Automatic speech recognition with deep neural networks for impaired speech
The final publication is available at https://link.springer.com/chapter/10.1007%2F978-3-319-49169-1_10Automatic Speech Recognition has reached almost human performance in some controlled scenarios. However, recognition of impaired speech is a difficult task for two main reasons: data is (i) scarce and (ii) heterogeneous. In this work we train different architectures on a database of dysarthric speech. A comparison between architectures shows that, even with a small database, hybrid DNN-HMM models outperform classical GMM-HMM according to word error rate measures. A DNN is able to improve the recognition word error rate a 13% for subjects with dysarthria with respect to the best classical architecture. This improvement is higher than the one given by other deep neural networks such as CNNs, TDNNs and LSTMs. All the experiments have been done with the Kaldi toolkit for speech recognition for which we have adapted several recipes to deal with dysarthric speech and work on the TORGO database. These recipes are publicly available.Peer ReviewedPostprint (author's final draft
On the efficient representation and execution of deep acoustic models
In this paper we present a simple and computationally efficient quantization
scheme that enables us to reduce the resolution of the parameters of a neural
network from 32-bit floating point values to 8-bit integer values. The proposed
quantization scheme leads to significant memory savings and enables the use of
optimized hardware instructions for integer arithmetic, thus significantly
reducing the cost of inference. Finally, we propose a "quantization aware"
training process that applies the proposed scheme during network training and
find that it allows us to recover most of the loss in accuracy introduced by
quantization. We validate the proposed techniques by applying them to a long
short-term memory-based acoustic model on an open-ended large vocabulary speech
recognition task.Comment: Accepted conference paper: "The Annual Conference of the
International Speech Communication Association (Interspeech), 2016
Ultra Dual-Path Compression For Joint Echo Cancellation And Noise Suppression
Echo cancellation and noise reduction are essential for full-duplex
communication, yet most existing neural networks have high computational costs
and are inflexible in tuning model complexity. In this paper, we introduce
time-frequency dual-path compression to achieve a wide range of compression
ratios on computational cost. Specifically, for frequency compression,
trainable filters are used to replace manually designed filters for dimension
reduction. For time compression, only using frame skipped prediction causes
large performance degradation, which can be alleviated by a post-processing
network with full sequence modeling. We have found that under fixed compression
ratios, dual-path compression combining both the time and frequency methods
will give further performance improvement, covering compression ratios from 4x
to 32x with little model size change. Moreover, the proposed models show
competitive performance compared with fast FullSubNet and DeepFilterNet. A demo
page can be found at
hangtingchen.github.io/ultra_dual_path_compression.github.io/.Comment: Accepted by Interspeech 202
3D-Speaker: A Large-Scale Multi-Device, Multi-Distance, and Multi-Dialect Corpus for Speech Representation Disentanglement
Disentangling uncorrelated information in speech utterances is a crucial
research topic within speech community. Different speech-related tasks focus on
extracting distinct speech representations while minimizing the affects of
other uncorrelated information. We present a large-scale speech corpus to
facilitate the research of speech representation disentanglement. 3D-Speaker
contains over 10,000 speakers, each of whom are simultaneously recorded by
multiple Devices, locating at different Distances, and some speakers are
speaking multiple Dialects. The controlled combinations of multi-dimensional
audio data yield a matrix of a diverse blend of speech representation
entanglement, thereby motivating intriguing methods to untangle them. The
multi-domain nature of 3D-Speaker also makes it a suitable resource to evaluate
large universal speech models and experiment methods of out-of-domain learning
and self-supervised learning. https://3dspeaker.github.io
Reducing the gap between streaming and non-streaming Transducer-based ASR by adaptive two-stage knowledge distillation
Transducer is one of the mainstream frameworks for streaming speech
recognition. There is a performance gap between the streaming and non-streaming
transducer models due to limited context. To reduce this gap, an effective way
is to ensure that their hidden and output distributions are consistent, which
can be achieved by hierarchical knowledge distillation. However, it is
difficult to ensure the distribution consistency simultaneously because the
learning of the output distribution depends on the hidden one. In this paper,
we propose an adaptive two-stage knowledge distillation method consisting of
hidden layer learning and output layer learning. In the former stage, we learn
hidden representation with full context by applying mean square error loss
function. In the latter stage, we design a power transformation based adaptive
smoothness method to learn stable output distribution. It achieved 19\%
relative reduction in word error rate, and a faster response for the first
token compared with the original streaming model in LibriSpeech corpus
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