Jorge, Javier ; Giménez, Adrià ; Silvestre-Cerdà, Joan Albert ; Civera, Jorge ; Sanchis, Albert ; Alfons, Juan Live Streaming Speech Recognition Using Deep Bidirectional LSTM Acoustic Models and Interpolated Language Models Journal Article IEEE/ACM Transactions on Audio, Speech, and Language Processing, 30 , pp. 148–161, 2021. Abstract | Links | BibTeX | Tags: acoustic modelling, Automatic Speech Recognition, decoding, language modelling, neural networks, streaming @article{Jorge2021b,
title = {Live Streaming Speech Recognition Using Deep Bidirectional LSTM Acoustic Models and Interpolated Language Models},
author = {Jorge, Javier and Giménez, Adrià and Silvestre-Cerdà, Joan Albert and Civera, Jorge and Sanchis, Albert and Juan Alfons},
doi = {10.1109/TASLP.2021.3133216},
year = {2021},
date = {2021-11-23},
journal = {IEEE/ACM Transactions on Audio, Speech, and Language Processing},
volume = {30},
pages = {148--161},
abstract = {Although Long-Short Term Memory (LSTM) networks and deep Transformers are now extensively used in offline ASR, it is unclear how best offline systems can be adapted to work with them under the streaming setup. After gaining considerable experience in this regard in recent years, in this paper we show how an optimized, low-latency streaming decoder can be built in which bidirectional LSTM acoustic models, together with general interpolated language models, can be nicely integrated with minimal perfomance degradation. In brief, our streaming decoder consists of a one-pass, real-time search engine relying on a limited-duration window sliding over time and a number of ad hoc acoustic and language model pruning techniques. Extensive empirical assessment is provided on truly streaming tasks derived from the well-known LibriSpeech and TED talks datasets, as well as from TV shows from a large Spanish broadcasting station.},
keywords = {acoustic modelling, Automatic Speech Recognition, decoding, language modelling, neural networks, streaming},
pubstate = {published},
tppubtype = {article}
}
Although Long-Short Term Memory (LSTM) networks and deep Transformers are now extensively used in offline ASR, it is unclear how best offline systems can be adapted to work with them under the streaming setup. After gaining considerable experience in this regard in recent years, in this paper we show how an optimized, low-latency streaming decoder can be built in which bidirectional LSTM acoustic models, together with general interpolated language models, can be nicely integrated with minimal perfomance degradation. In brief, our streaming decoder consists of a one-pass, real-time search engine relying on a limited-duration window sliding over time and a number of ad hoc acoustic and language model pruning techniques. Extensive empirical assessment is provided on truly streaming tasks derived from the well-known LibriSpeech and TED talks datasets, as well as from TV shows from a large Spanish broadcasting station. |
Jorge, Javier; Giménez, Adrià; Iranzo-Sánchez, Javier; Silvestre-Cerdà, Joan Albert; Civera, Jorge; Sanchis, Albert; Juan, Alfons LSTM-Based One-Pass Decoder for Low-Latency Streaming Inproceedings Proc. of 45th Intl. Conf. on Acoustics, Speech, and Signal Processing (ICASSP 2020), pp. 7814–7818, Barcelona (Spain), 2020. Abstract | Links | BibTeX | Tags: acoustic modeling, Automatic Speech Recognition, decoding, Language Modeling, streaming @inproceedings{Jorge2020,
title = {LSTM-Based One-Pass Decoder for Low-Latency Streaming},
author = {Javier Jorge and Adrià Giménez and Javier Iranzo-Sánchez and Joan Albert Silvestre-Cerdà and Jorge Civera and Albert Sanchis and Alfons Juan},
url = {https://www.mllp.upv.es/wp-content/uploads/2020/01/jorge2020_preprint.pdf
https://doi.org/10.1109/ICASSP40776.2020.9054267},
year = {2020},
date = {2020-01-01},
booktitle = {Proc. of 45th Intl. Conf. on Acoustics, Speech, and Signal Processing (ICASSP 2020)},
pages = {7814--7818},
address = {Barcelona (Spain)},
abstract = {Current state-of-the-art models based on Long-Short Term Memory (LSTM) networks have been extensively used in ASR to improve performance. However, using LSTMs under a streaming setup is not straightforward due to real-time constraints. In this paper we present a novel streaming decoder that includes a bidirectional LSTM acoustic model as well as an unidirectional LSTM language model to perform the decoding efficiently while keeping the performance comparable to that of an off-line setup. We perform a one-pass decoding using a sliding window scheme for a bidirectional LSTM acoustic model and an LSTM language model. This has been implemented and assessed under a pure streaming setup, and deployed into our production systems. We report WER and latency figures for the well-known LibriSpeech and TED-LIUM tasks, obtaining competitive WER results with low-latency responses.},
keywords = {acoustic modeling, Automatic Speech Recognition, decoding, Language Modeling, streaming},
pubstate = {published},
tppubtype = {inproceedings}
}
Current state-of-the-art models based on Long-Short Term Memory (LSTM) networks have been extensively used in ASR to improve performance. However, using LSTMs under a streaming setup is not straightforward due to real-time constraints. In this paper we present a novel streaming decoder that includes a bidirectional LSTM acoustic model as well as an unidirectional LSTM language model to perform the decoding efficiently while keeping the performance comparable to that of an off-line setup. We perform a one-pass decoding using a sliding window scheme for a bidirectional LSTM acoustic model and an LSTM language model. This has been implemented and assessed under a pure streaming setup, and deployed into our production systems. We report WER and latency figures for the well-known LibriSpeech and TED-LIUM tasks, obtaining competitive WER results with low-latency responses. |