612 research outputs found

    Deciphering Speech: a Zero-Resource Approach to Cross-Lingual Transfer in ASR

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    We present a method for cross-lingual training an ASR system using absolutely no transcribed training data from the target language, and with no phonetic knowledge of the language in question. Our approach uses a novel application of a decipherment algorithm, which operates given only unpaired speech and text data from the target language. We apply this decipherment to phone sequences generated by a universal phone recogniser trained on out-of-language speech corpora, which we follow with flat-start semi-supervised training to obtain an acoustic model for the new language. To the best of our knowledge, this is the first practical approach to zero-resource cross-lingual ASR which does not rely on any hand-crafted phonetic information. We carry out experiments on read speech from the GlobalPhone corpus, and show that it is possible to learn a decipherment model on just 20 minutes of data from the target language. When used to generate pseudo-labels for semi-supervised training, we obtain WERs that range from 32.5% to just 1.9% absolute worse than the equivalent fully supervised models trained on the same data.Comment: Submitted to Interspeech 202

    Deciphering Speech: a Zero-Resource Approach to Cross-Lingual Transfer in ASR

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    Using closely-related language to build an ASR for a very under-resourced language: Iban

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    International audienceThis paper describes our work on automatic speech recognition system (ASR) for an under-resourced language, Iban, a language that is mainly spoken in Sarawak, Malaysia. We collected 8 hours of data to begin this study due to no resources for ASR exist. We employed bootstrapping techniques involving a closely-related language for rapidly building and improve an Iban system. First, we used already available data from Malay, a local dominant language in Malaysia, to bootstrap grapheme-to-phoneme system (G2P) for the target language. We also built various types of G2Ps, including a grapheme-based and an English G2P, to produce different versions of dictionaries. We tested all of the dictionaries on the Iban ASR to provide us the best version. Second, we improved the baseline GMM system word error rate (WER) result by utilizing subspace Gaussian mixture models (SGMM). To test, we set two levels of data sparseness on Iban data; 7 hours and 1 hour transcribed speech. We investigated cross-lingual SGMM where the shared parameters were obtained either in monolingual or multilingual fashion and then applied to the target language for training. Experiments on out-of-language data, English and Malay, as source languages result in lower WERs when Iban data is very limited

    Using Resources from a Closely-related Language to Develop ASR for a Very Under-resourced Language: A Case Study for Iban

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    International audienceThis paper presents our strategies for developing an automatic speech recognition system for Iban, an under-resourced language. We faced several challenges such as no pronunciation dictionary and lack of training material for building acoustic models. To overcome these problems, we proposed approaches which exploit resources from a closely-related language (Malay). We developed a semi-supervised method for building the pronunciation dictionary and applied cross-lingual strategies for improving acoustic models trained with very limited training data. Both approaches displayed very encouraging results, which show that data from a closely-related language, if available, can be exploited to build ASR for a new language. In the final part of the paper, we present a zero-shot ASR using Malay resources that can be used as an alternative method for transcribing Iban speech

    Multilingual audio information management system based on semantic knowledge in complex environments

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    This paper proposes a multilingual audio information management system based on semantic knowledge in complex environments. The complex environment is defined by the limited resources (financial, material, human, and audio resources); the poor quality of the audio signal taken from an internet radio channel; the multilingual context (Spanish, French, and Basque that is in under-resourced situation in some areas); and the regular appearance of cross-lingual elements between the three languages. In addition to this, the system is also constrained by the requirements of the local multilingual industrial sector. We present the first evolutionary system based on a scalable architecture that is able to fulfill these specifications with automatic adaptation based on automatic semantic speech recognition, folksonomies, automatic configuration selection, machine learning, neural computing methodologies, and collaborative networks. As a result, it can be said that the initial goals have been accomplished and the usability of the final application has been tested successfully, even with non-experienced users.This work is being funded by Grants: TEC201677791-C4 from Plan Nacional de I + D + i, Ministry of Economic Affairs and Competitiveness of Spain and from the DomusVi Foundation Kms para recorder, the Basque Government (ELKARTEK KK-2018/00114, GEJ IT1189-19, the Government of Gipuzkoa (DG18/14 DG17/16), UPV/EHU (GIU19/090), COST ACTION (CA18106, CA15225)

    Automatic Speech Recognition for Low-resource Languages and Accents Using Multilingual and Crosslingual Information

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    This thesis explores methods to rapidly bootstrap automatic speech recognition systems for languages, which lack resources for speech and language processing. We focus on finding approaches which allow using data from multiple languages to improve the performance for those languages on different levels, such as feature extraction, acoustic modeling and language modeling. Under application aspects, this thesis also includes research work on non-native and Code-Switching speech
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