740 research outputs found
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.
Permission from IEEE must be obtained for all other uses, in any current or
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this work in other works.
Fast and Accurate OOV Decoder on High-Level Features
This work proposes a novel approach to out-of-vocabulary (OOV) keyword search
(KWS) task. The proposed approach is based on using high-level features from an
automatic speech recognition (ASR) system, so called phoneme posterior based
(PPB) features, for decoding. These features are obtained by calculating
time-dependent phoneme posterior probabilities from word lattices, followed by
their smoothing. For the PPB features we developed a special novel very fast,
simple and efficient OOV decoder. Experimental results are presented on the
Georgian language from the IARPA Babel Program, which was the test language in
the OpenKWS 2016 evaluation campaign. The results show that in terms of maximum
term weighted value (MTWV) metric and computational speed, for single ASR
systems, the proposed approach significantly outperforms the state-of-the-art
approach based on using in-vocabulary proxies for OOV keywords in the indexed
database. The comparison of the two OOV KWS approaches on the fusion results of
the nine different ASR systems demonstrates that the proposed OOV decoder
outperforms the proxy-based approach in terms of MTWV metric given the
comparable processing speed. Other important advantages of the OOV decoder
include extremely low memory consumption and simplicity of its implementation
and parameter optimization.Comment: Interspeech 2017, August 2017, Stockholm, Sweden. 201
Multilingual representations for low resource speech recognition and keyword search
© 2015 IEEE. This paper examines the impact of multilingual (ML) acoustic representations on Automatic Speech Recognition (ASR) and keyword search (KWS) for low resource languages in the context of the OpenKWS15 evaluation of the IARPA Babel program. The task is to develop Swahili ASR and KWS systems within two weeks using as little as 3 hours of transcribed data. Multilingual acoustic representations proved to be crucial for building these systems under strict time constraints. The paper discusses several key insights on how these representations are derived and used. First, we present a data sampling strategy that can speed up the training of multilingual representations without appreciable loss in ASR performance. Second, we show that fusion of diverse multilingual representations developed at different LORELEI sites yields substantial ASR and KWS gains. Speaker adaptation and data augmentation of these representations improves both ASR and KWS performance (up to 8.7% relative). Third, incorporating un-transcribed data through semi-supervised learning, improves WER and KWS performance. Finally, we show that these multilingual representations significantly improve ASR and KWS performance (relative 9% for WER and 5% for MTWV) even when forty hours of transcribed audio in the target language is available. Multilingual representations significantly contributed to the LORELEI KWS systems winning the OpenKWS15 evaluation
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