8 research outputs found

    Automatic Speech Recognition without Transcribed Speech or Pronunciation Lexicons

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    Rapid deployment of automatic speech recognition (ASR) in new languages, with very limited data, is of great interest and importance for intelligence gathering, as well as for humanitarian assistance and disaster relief (HADR). Deploying ASR systems in these languages often relies on cross-lingual acoustic modeling followed by supervised adaptation and almost always assumes that either a pronunciation lexicon using the International Phonetic Alphabet (IPA), and/or some amount of transcribed speech exist in the new language of interest. For many languages, neither requirement is generally true -- only a limited amount of text and untranscribed audio is available. This work focuses specifically on scalable techniques for building ASR systems in most languages without any existing transcribed speech or pronunciation lexicons. We first demonstrate how cross-lingual acoustic model transfer, when phonemic pronunciation lexicons do exist in a new language, can significantly reduce the need for target-language transcribed speech. We then explore three methods for handling languages without a pronunciation lexicon. First we examine the effectiveness of graphemic acoustic model transfer, which allows for pronunciation lexicons to be trivially constructed. We then present two methods for rapid construction of phonemic pronunciation lexicons based on submodular selection of a small set of words for manual annotation, or words from other languages for which we have IPA pronunciations. We also explore techniques for training sequence-to-sequence models with very small amounts of data by transferring models trained on other languages, and leveraging large unpaired text corpora in training. Finally, as an alternative to acoustic model transfer, we present a novel hybrid generative/discriminative semi-supervised training framework that merges recent progress in Energy Based Models (EBMs) as well as lattice-free maximum mutual information (LF-MMI) training, capable of making use of purely untranscribed audio. Together, these techniques enabled ASR capabilities that supported triage of spoken communications in real-world HADR work-flows in many languages using fewer than 30 minutes of transcribed speech. These techniques were successfully applied in multiple NIST evaluations and were among the top-performing systems in each evaluation

    Adaptation and Augmentation: Towards Better Rescoring Strategies for Automatic Speech Recognition and Spoken Term Detection

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    Selecting the best prediction from a set of candidates is an essential problem for many spoken language processing tasks, including automatic speech recognition (ASR) and spoken keyword spotting (KWS). Generally, the selection is determined by a confidence score assigned to each candidate. Calibrating these confidence scores (i.e., rescoring them) could make better selections and improve the system performance. This dissertation focuses on using tailored language models to rescore ASR hypotheses as well as keyword search results for ASR-based KWS. This dissertation introduces three kinds of rescoring techniques: (1) Freezing most model parameters while fine-tuning the output layer in order to adapt neural network language models (NNLMs) from the written domain to the spoken domain. Experiments on a large-scale Italian corpus show a 30.2% relative reduction in perplexity at the word-cluster level and a 2.3% relative reduction in WER in a state-of-the-art Italian ASR system. (2) Incorporating source application information associated with speech queries. By exploring a range of adaptation model architectures, we achieve a 21.3% relative reduction in perplexity compared to a fine-tuned baseline. Initial experiments using a state-of-the-art Italian ASR system show a 3.0% relative reduction in WER on top of an unadapted 5-gram LM. In addition, human evaluations show significant improvements by using the source application information. (3) Marrying machine learning algorithms (classification and ranking) with a variety of signals to rescore keyword search results in the context of KWS for low-resource languages. These systems, built for the IARPA BABEL Program, enhance search performance in terms of maximum term-weighted value (MTWV) across six different low-resource languages: Vietnamese, Tagalog, Pashto, Turkish, Zulu and Tamil

    IberSPEECH 2020: XI Jornadas en Tecnología del Habla and VII Iberian SLTech

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    IberSPEECH2020 is a two-day event, bringing together the best researchers and practitioners in speech and language technologies in Iberian languages to promote interaction and discussion. The organizing committee has planned a wide variety of scientific and social activities, including technical paper presentations, keynote lectures, presentation of projects, laboratories activities, recent PhD thesis, discussion panels, a round table, and awards to the best thesis and papers. The program of IberSPEECH2020 includes a total of 32 contributions that will be presented distributed among 5 oral sessions, a PhD session, and a projects session. To ensure the quality of all the contributions, each submitted paper was reviewed by three members of the scientific review committee. All the papers in the conference will be accessible through the International Speech Communication Association (ISCA) Online Archive. Paper selection was based on the scores and comments provided by the scientific review committee, which includes 73 researchers from different institutions (mainly from Spain and Portugal, but also from France, Germany, Brazil, Iran, Greece, Hungary, Czech Republic, Ucrania, Slovenia). Furthermore, it is confirmed to publish an extension of selected papers as a special issue of the Journal of Applied Sciences, “IberSPEECH 2020: Speech and Language Technologies for Iberian Languages”, published by MDPI with fully open access. In addition to regular paper sessions, the IberSPEECH2020 scientific program features the following activities: the ALBAYZIN evaluation challenge session.Red Española de Tecnologías del Habla. Universidad de Valladoli

    Augmenting automatic speech recognition and search models for spoken content retrieval

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    Spoken content retrieval (SCR) is a process to provide a user with spoken documents in which the user is potentially interested. Unlike textual documents, searching through speech is not trivial due to its representation. Generally, automatic speech recognition (ASR) is used to transcribe spoken content such as user-generated videos and podcast episodes into transcripts before search operations are performed. Despite recent improvements in ASR, transcription errors can still be present in automatic transcripts. This is in particular when ASR is applied to out-of-domain data or speech with background noise. This thesis explores improvement of ASR systems and search models for enhanced SCR on user-generated spoken content. There are three topics explored in this thesis. Firstly, the use of multimodal signals for ASR is investigated. This is motivated to integrate background contexts of spoken content into ASR. Integration of visual signals and document metadata into ASR is hypothesised to produce transcripts more aligned to background contexts of speech. Secondly, the use of semi-supervised training and content genre information from metadata are exploited for ASR. This approach is motivated to mitigate the transcription errors caused by recognition of out-of-domain speech. Thirdly, the use of neural models and the model extension using N-best ASR transcripts are investigated. Using ASR N-best transcripts instead of 1-best for search models is motivated because "key terms" missed in 1-best can be present in the N-best transcripts. A series of experiments are conducted to examine those approaches to improvement of ASR systems and search models. The findings suggest that semi-supervised training bring practical improvement of ASR systems for SCR and the use of neural ranking models in particular with N-best transcripts improve the result of known-item search over the baseline BM25 model
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