Multilingual corpora for speech-to-speech translation research

Kikui, Genichiro; Takezawa, Toshiyuki; Yamamoto, Seiichi

doi:10.21437/interspeech.2004-157

Cited by 15 publications

(7 citation statements)

References 9 publications

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“…Dataset: We perform experiments on two widelyused tasks for the English-to-Japanese language pair: KFTT (Neubig, 2011) and BTEC (Kikui et al, 2003). KFTT is a collection of Wikipedia article about city of Kyoto and BTEC is a travel conversation corpus.…”

Section: Settingsmentioning

confidence: 99%

Incorporating Discrete Translation Lexicons into Neural Machine Translation

Arthur

Neubig

Nakamura

2016

Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

163

139

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Neural machine translation (NMT) often makes mistakes in translating low-frequency content words that are essential to understanding the meaning of the sentence. We propose a method to alleviate this problem by augmenting NMT systems with discrete translation lexicons that efficiently encode translations of these low-frequency words. We describe a method to calculate the lexicon probability of the next word in the translation candidate by using the attention vector of the NMT model to select which source word lexical probabilities the model should focus on. We test two methods to combine this probability with the standard NMT probability: (1) using it as a bias, and (2) linear interpolation. Experiments on two corpora show an improvement of 2.0-2.3 BLEU and 0.13-0.44 NIST score, and faster convergence time.

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Section: Settingsmentioning

confidence: 99%

Incorporating Discrete Translation Lexicons into Neural Machine Translation

Arthur

Neubig

Nakamura

2016

Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

163

139

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“…We also translated fifty Indonesian sentences from the ATR basic travel expression corpus (BTEC) (Kikui et al, 2003) dataset into these ethnic languages by native speakers of the corresponding language. We created 275 sentences for each ethnic language, comprised of 1,100 text transcriptions.…”

Section: Textmentioning

confidence: 99%

Cross-Lingual Machine Speech Chain for Javanese, Sundanese, Balinese, and Bataks Speech Recognition and Synthesis

Novitasari¹,

Tjandra²,

Sakti³

et al. 2020

Preprint

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Even though over seven hundred ethnic languages are spoken in Indonesia, the available technology remains limited that could support communication within indigenous communities as well as with people outside the villages. As a result, indigenous communities still face isolation due to cultural barriers; languages continue to disappear. To accelerate communication, speech-to-speech translation (S2ST) technology is one approach that can overcome language barriers. However, S2ST systems require machine translation (MT), speech recognition (ASR), and synthesis (TTS) that rely heavily on supervised training and a broad set of language resources that can be difficult to collect from ethnic communities. Recently, a machine speech chain mechanism was proposed to enable ASR and TTS to assist each other in semi-supervised learning. The framework was initially implemented only for monolingual languages. In this study, we focus on developing speech recognition and synthesis for these Indonesian ethnic languages: Javanese, Sundanese, Balinese, and Bataks. We first separately train ASR and TTS of standard Indonesian in supervised training. We then develop ASR and TTS of ethnic languages by utilizing Indonesian ASR and TTS in a cross-lingual machine speech chain framework with only text or only speech data removing the need for paired speech-text data of those ethnic languages.

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“…In this paper, we ran our experiment based on the Basic Travel Expression (BTEC) corpus [28,29] that has several language pairs . We chose two tasks: French-to-English and Japanese-to-English.…”

Section: Datasetmentioning

confidence: 99%

Speech-to-Speech Translation Between Untranscribed Unknown Languages

Tjandra

Sakti

Nakamura

2019

2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)

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In this paper, we explore a method for training speech-to-speech translation tasks without any transcription or linguistic supervision. Our proposed method consists of two steps: First, we train and generate discrete representation with unsupervised term discovery with a discrete quantized autoencoder. Second, we train a sequence-tosequence model that directly maps the source language speech to the target languages discrete representation. Our proposed method can directly generate target speech without any auxiliary or pre-training steps with a source or target transcription. To the best of our knowledge, this is the first work that performed pure speech-to-speech translation between untranscribed unknown languages.

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Multilingual corpora for speech-to-speech translation research

Cited by 15 publications

References 9 publications

Incorporating Discrete Translation Lexicons into Neural Machine Translation

Incorporating Discrete Translation Lexicons into Neural Machine Translation

Cross-Lingual Machine Speech Chain for Javanese, Sundanese, Balinese, and Bataks Speech Recognition and Synthesis

Speech-to-Speech Translation Between Untranscribed Unknown Languages

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