Accessibility Automatic Speech Recognition Computer-assisted transcription Confidence measures Deep Neural Networks Docencia en Red Education language model adaptation Language Modeling Language Technologies Length modelling Log-linear models Machine Translation Massive Adaptation Models basats en seqüències de paraules Models log-lineals Multilingualism Neural Machine Translation Opencast Matterhorn Polimedia Sliding window Speaker adaptation Speech Recognition Speech Translation Statistical machine translation streaming text-to-speech transcripciones video lecture repositories Video Lectures
2023 |
Baquero Arnal, Pau Transformer models for Machine Translation and Streaming Automatic Speech Recognition PhD Thesis Universitat Politècnica de València, 2023, (Advisors: Alfons Juan Ciscar and Hermann Ney). Abstract | Links | BibTeX | Tags: Automatic Speech Recognition, Neural Machine Translation, Transformer, Transformer Language Model @phdthesis{Arnal2023, title = {Transformer models for Machine Translation and Streaming Automatic Speech Recognition}, author = {Baquero Arnal, Pau}, url = {https://doi.org/10.4995/Thesis/10251/193680 https://www.upv.es/pls/oalu/sic_ted.mostrar_tesis?p_num_reg=12917}, year = {2023}, date = {2023-01-01}, school = {Universitat Politècnica de València}, abstract = {Natural language processing (NLP) is a set of fundamental computing prob- lems with immense applicability, as language is the natural communication vehicle for people. NLP, along with many other computer technologies, has been revolutionized in recent years by the impact of deep learning. This thesis is centered around two keystone problems for NLP: machine translation (MT) and automatic speech recognition (ASR); and a common deep neural architec- ture, the Transformer, that is leveraged to improve the technical solutions for some MT and ASR applications. ASR and MT can be utilized to produce cost-effective, high-quality multilin- gual texts for a wide array of media. Particular applications pursued in this thesis are that of news translation or that of automatic live captioning of tele- vision broadcasts. ASR and MT can also be combined with each other, for instance generating automatic translated subtitles from audio, or augmented with other NLP solutions: text summarization to produce a summary of a speech, or speech synthesis to create an automatic translated dubbing, for in- stance. These other applications fall out of the scope of this thesis, but can profit from the contributions that it contains, as they help to improve the performance of the automatic systems on which they depend. This thesis contains an application of the Transformer architecture to MT as it was originally conceived, achieving state-of-the-art results in similar language translation. In successive chapters, this thesis covers the adaptation of the Transformer as a language model for streaming hybrid ASR systems. After- wards, it describes how we applied the developed technology for a specific use case in television captioning by participating in a competitive challenge and achieving the first position by a large margin. We also show that the gains came mostly from the improvement in technology capabilities over two years including that of the Transformer language model adapted for streaming, and the data component was minor.}, note = {Advisors: Alfons Juan Ciscar and Hermann Ney}, keywords = {Automatic Speech Recognition, Neural Machine Translation, Transformer, Transformer Language Model}, pubstate = {published}, tppubtype = {phdthesis} } Natural language processing (NLP) is a set of fundamental computing prob- lems with immense applicability, as language is the natural communication vehicle for people. NLP, along with many other computer technologies, has been revolutionized in recent years by the impact of deep learning. This thesis is centered around two keystone problems for NLP: machine translation (MT) and automatic speech recognition (ASR); and a common deep neural architec- ture, the Transformer, that is leveraged to improve the technical solutions for some MT and ASR applications. ASR and MT can be utilized to produce cost-effective, high-quality multilin- gual texts for a wide array of media. Particular applications pursued in this thesis are that of news translation or that of automatic live captioning of tele- vision broadcasts. ASR and MT can also be combined with each other, for instance generating automatic translated subtitles from audio, or augmented with other NLP solutions: text summarization to produce a summary of a speech, or speech synthesis to create an automatic translated dubbing, for in- stance. These other applications fall out of the scope of this thesis, but can profit from the contributions that it contains, as they help to improve the performance of the automatic systems on which they depend. This thesis contains an application of the Transformer architecture to MT as it was originally conceived, achieving state-of-the-art results in similar language translation. In successive chapters, this thesis covers the adaptation of the Transformer as a language model for streaming hybrid ASR systems. After- wards, it describes how we applied the developed technology for a specific use case in television captioning by participating in a competitive challenge and achieving the first position by a large margin. We also show that the gains came mostly from the improvement in technology capabilities over two years including that of the Transformer language model adapted for streaming, and the data component was minor. |
2020 |
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. |