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. |
Baquero-Arnal, Pau ; Jorge, Javier ; Giménez, Adrià ; Silvestre-Cerdà, Joan Albert ; Iranzo-Sánchez, Javier ; Sanchis, Albert ; Civera, Jorge ; Juan, Alfons Improved Hybrid Streaming ASR with Transformer Language Models Inproceedings Proc. of 21st Annual Conf. of the Intl. Speech Communication Association (InterSpeech 2020), pp. 2127–2131, Shanghai (China), 2020. Abstract | Links | BibTeX | Tags: hybrid ASR, language models, streaming, Transformer @inproceedings{Baquero-Arnal2020,
title = {Improved Hybrid Streaming ASR with Transformer Language Models},
author = {Baquero-Arnal, Pau and Jorge, Javier and Giménez, Adrià and Silvestre-Cerdà, Joan Albert and Iranzo-Sánchez, Javier and Sanchis, Albert and Civera, Jorge and Juan, Alfons},
url = {http://dx.doi.org/10.21437/Interspeech.2020-2770},
year = {2020},
date = {2020-01-01},
booktitle = {Proc. of 21st Annual Conf. of the Intl. Speech Communication Association (InterSpeech 2020)},
pages = {2127--2131},
address = {Shanghai (China)},
abstract = {Streaming ASR is gaining momentum due to its wide applicability, though it is still unclear how best to come close to the accuracy of state-of-the-art off-line ASR systems when the output must come within a short delay after the incoming audio stream. Following our previous work on streaming one-pass decoding with hybrid ASR systems and LSTM language models, in this work we report further improvements by replacing LSTMs with Transformer models. First, two key ideas are discussed so as to run these models fast during inference. Then, empirical results on LibriSpeech and TED-LIUM are provided showing that Transformer language models lead to improved recognition rates on both tasks. ASR systems obtained in this work can be seamlessly transfered to a streaming setup with minimal quality losses. Indeed, to the best of our knowledge, no better results have been reported on these tasks when assessed under a streaming setup.},
keywords = {hybrid ASR, language models, streaming, Transformer},
pubstate = {published},
tppubtype = {inproceedings}
}
Streaming ASR is gaining momentum due to its wide applicability, though it is still unclear how best to come close to the accuracy of state-of-the-art off-line ASR systems when the output must come within a short delay after the incoming audio stream. Following our previous work on streaming one-pass decoding with hybrid ASR systems and LSTM language models, in this work we report further improvements by replacing LSTMs with Transformer models. First, two key ideas are discussed so as to run these models fast during inference. Then, empirical results on LibriSpeech and TED-LIUM are provided showing that Transformer language models lead to improved recognition rates on both tasks. ASR systems obtained in this work can be seamlessly transfered to a streaming setup with minimal quality losses. Indeed, to the best of our knowledge, no better results have been reported on these tasks when assessed under a streaming setup. |