9,498 research outputs found
Mutual-learning sequence-level knowledge distillation for automatic speech recognition
Automatic speech recognition (ASR) is a crucial technology for man-machine interaction. End-to-end models have been studied recently in deep learning for ASR. However, these models are not suitable for the practical application of ASR due to their large model sizes and computation costs. To address this issue, we propose a novel mutual-learning sequence-level knowledge distillation framework enjoying distinct student structures for ASR. Trained mutually and simultaneously, each student learns not only from the pre-trained teacher but also from its distinct peers, which can improve the generalization capability of the whole network, through making up for the insufficiency of each student and bridging the gap between each student and the teacher. Extensive experiments on the TIMIT and large LibriSpeech corpuses show that, compared with the state-of-the-art methods, the proposed method achieves an excellent balance between recognition accuracy and model compression
Low-Latency Sequence-to-Sequence Speech Recognition and Translation by Partial Hypothesis Selection
Encoder-decoder models provide a generic architecture for
sequence-to-sequence tasks such as speech recognition and translation. While
offline systems are often evaluated on quality metrics like word error rates
(WER) and BLEU, latency is also a crucial factor in many practical use-cases.
We propose three latency reduction techniques for chunk-based incremental
inference and evaluate their efficiency in terms of accuracy-latency trade-off.
On the 300-hour How2 dataset, we reduce latency by 83% to 0.8 second by
sacrificing 1% WER (6% rel.) compared to offline transcription. Although our
experiments use the Transformer, the hypothesis selection strategies are
applicable to other encoder-decoder models. To avoid expensive re-computation,
we use a unidirectionally-attending encoder. After an adaptation procedure to
partial sequences, the unidirectional model performs on-par with the original
model. We further show that our approach is also applicable to low-latency
speech translation. On How2 English-Portuguese speech translation, we reduce
latency to 0.7 second (-84% rel.) while incurring a loss of 2.4 BLEU points (5%
rel.) compared to the offline system
Teacher-Student Architecture for Knowledge Distillation: A Survey
Although Deep neural networks (DNNs) have shown a strong capacity to solve
large-scale problems in many areas, such DNNs are hard to be deployed in
real-world systems due to their voluminous parameters. To tackle this issue,
Teacher-Student architectures were proposed, where simple student networks with
a few parameters can achieve comparable performance to deep teacher networks
with many parameters. Recently, Teacher-Student architectures have been
effectively and widely embraced on various knowledge distillation (KD)
objectives, including knowledge compression, knowledge expansion, knowledge
adaptation, and knowledge enhancement. With the help of Teacher-Student
architectures, current studies are able to achieve multiple distillation
objectives through lightweight and generalized student networks. Different from
existing KD surveys that primarily focus on knowledge compression, this survey
first explores Teacher-Student architectures across multiple distillation
objectives. This survey presents an introduction to various knowledge
representations and their corresponding optimization objectives. Additionally,
we provide a systematic overview of Teacher-Student architectures with
representative learning algorithms and effective distillation schemes. This
survey also summarizes recent applications of Teacher-Student architectures
across multiple purposes, including classification, recognition, generation,
ranking, and regression. Lastly, potential research directions in KD are
investigated, focusing on architecture design, knowledge quality, and
theoretical studies of regression-based learning, respectively. Through this
comprehensive survey, industry practitioners and the academic community can
gain valuable insights and guidelines for effectively designing, learning, and
applying Teacher-Student architectures on various distillation objectives.Comment: 20 pages. arXiv admin note: substantial text overlap with
arXiv:2210.1733
Lessons from Building Acoustic Models with a Million Hours of Speech
This is a report of our lessons learned building acoustic models from 1
Million hours of unlabeled speech, while labeled speech is restricted to 7,000
hours. We employ student/teacher training on unlabeled data, helping scale out
target generation in comparison to confidence model based methods, which
require a decoder and a confidence model. To optimize storage and to
parallelize target generation, we store high valued logits from the teacher
model. Introducing the notion of scheduled learning, we interleave learning on
unlabeled and labeled data. To scale distributed training across a large number
of GPUs, we use BMUF with 64 GPUs, while performing sequence training only on
labeled data with gradient threshold compression SGD using 16 GPUs. Our
experiments show that extremely large amounts of data are indeed useful; with
little hyper-parameter tuning, we obtain relative WER improvements in the 10 to
20% range, with higher gains in noisier conditions.Comment: "Copyright 2019 IEEE. Personal use of this material is permitted.
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