Cross-Lingual Language Modeling for Low-Resource Speech Recognition

Xu, Ping; Fung, Pascale

doi:10.1109/tasl.2013.2244088

Cited by 4 publications

References 26 publications

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Bilingual Automatic Speech Recognition: A Review, Taxonomy and Open Challenges

et al. 2023

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In this technological era, smart and intelligent systems that are integrated with artificial intelligence (AI) techniques, algorithms, tools, and technologies, have impact on various aspects of our daily life. Communication and interaction between human and machine using speech becomes increasingly important, since it is an obvious substitute for keyboards and screens in the communication process. Therefore, numerous technologies take advantage of speech such as Automatic Speech Recognition (ASR), where human natural speech for many languages is used as the mean to interact with machines. Majority of the related works for ASR concentrate on the development and evaluation of ASR systems that serve a single language (monolingual) only, such as Arabic, English, Chinese, French, and many others. However, research attempts that combine multiple languages (bilingual and multilingual) during the development and evaluation of ASR systems are very limited. This paper aims to provide comprehensive research background and fundamentals of bilingual ASR, and related works that have combined two languages for ASR tasks from 2010 through 2021. It also formulates research taxonomy and discusses open challenges to bilingual ASR research. Based on our literature investigation, it is clear that bilingual ASR using deep learning approach is highly demanded and is able to provide acceptable performance. In addition, many combinations of two languages such as Arabic-English, Arabic-Malay, and others, are not attempted yet by the research community, which can open new research opportunities. Finally, it is clear that ASR research is moving towards not only bilingual ASR, but also multilingual ASR.

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Bilingual Automatic Speech Recognition: A Review, Taxonomy and Open Challenges

et al. 2023

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Machine translation based data augmentation for Cantonese keyword spotting

Huang

Gorin

Gauvain

et al. 2016

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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This paper presents a method to improve a language model for a limited-resourced language using statistical machine translation from a related language to generate data for the target language. In this work, the machine translation model is trained on a corpus of parallel Mandarin-Cantonese subtitles and used to translate a large set of Mandarin conversational telephone transcripts to Cantonese, which has limited resources. The translated transcripts are used to train a more robust language model for speech recognition and for keyword search in Cantonese conversational telephone speech. This method enables the keyword search system to detect 1.5 times more out-of-vocabulary words, and achieve 1.7% absolute improvement on actual term-weighted value.

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A cross-lingual adaptation approach for rapid development of speech recognizers for learning disabled users

Bohac

Kucharova

Callejas

et al. 2014

J AUDIO SPEECH MUSIC PROC.

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Building a voice-operated system for learning disabled users is a difficult task that requires a considerable amount of time and effort. Due to the wide spectrum of disabilities and their different related phonopathies, most approaches available are targeted to a specific pathology. This may improve their accuracy for some users, but makes them unsuitable for others. In this paper, we present a cross-lingual approach to adapt a general-purpose modular speech recognizer for learning disabled people. The main advantage of this approach is that it allows rapid and cost-effective development by taking the already built speech recognition engine and its modules, and utilizing existing resources for standard speech in different languages for the recognition of the users' atypical voices. Although the recognizers built with the proposed technique obtain lower accuracy rates than those trained for specific pathologies, they can be used by a wide population and developed more rapidly, which makes it possible to design various types of speech-based applications accessible to learning disabled users.

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Cross-Lingual Language Modeling for Low-Resource Speech Recognition

Cited by 4 publications

References 26 publications

Bilingual Automatic Speech Recognition: A Review, Taxonomy and Open Challenges

Bilingual Automatic Speech Recognition: A Review, Taxonomy and Open Challenges

Machine translation based data augmentation for Cantonese keyword spotting

A cross-lingual adaptation approach for rapid development of speech recognizers for learning disabled users

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