The ATR Multilingual Speech-to-Speech Translation System

By Satoshi Nakamura, K. Markov, H. Nakaiwa, G. Kikui, Hisashi Kawai, T. Jitsuhiro, Jin-Song Zhang, H. Yamamoto, Eiichiro Sumita, Seiichi Yamamoto. 2006

178 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Machine Translation, Speech & Audio

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Tao Qin
2. Tie-Yan Liu
3. Xu Tan
4. Zhou Zhao
5. Wei-Ning Hsu
6. Juan Pino
7. Changhan Wang
8. Ye Jia

In Machine Translation

1. Ye Jia
2. Michelle Tadmor Ramanovich
3. Tal Remez
4. Juan Pino
5. Changhan Wang
6. Roi Pomerantz
7. Yossi Adi
8. Yun Tang

In Speech & Audio

1. Xu Tan
2. Tao Qin
3. Ye Jia
4. Tie-Yan Liu
5. Yi Ren
6. Wei-Ning Hsu
7. Zhou Zhao
8. Yossi Adi

Citations in the corpus

• AudioPaLM: A Large Language Model That Can Speak and Listen (2023, neutral): “Conventional automatic speech-to-speech translation systems are typically composed of a cascade of three components: automatic speech recognition (ASR), text-to-text machine translation (MT), and text-to-speech (TTS) synthesis [Lavie et al., 1997, Wahlster, 2000, Nakamura et al., 2006].”
• Speak Foreign Languages with Your Own Voice: Cross-Lingual Neural Codec Language Modeling (2023, neutral): “The initial research and application mainly focus on cascaded S2ST systems [Lavie et al., 1997, Nakamura et al., 2006, Wahlster, 2013], consisting of speech recognition (ASR), machine translation (MT), and speech synthesis (TTS) models.”
• Enhanced Direct Speech-to-Speech Translation Using Self-supervised Pre-training and Data Augmentation (2022, background): “Compared to conventional cascaded approaches [7, 8], which take advantage of automatic speech recognition (ASR), machine translation (MT) or end-to-end speech-to-text translation (S2T) followed by text-to-speech synthesis (TTS), direct S2ST has the advantage of faster inference [4] and can support t”
• Textless Speech-to-Speech Translation on Real Data (2021, neutral): “Conventional S2ST systems (Lavie et al., 1997; Nakamura et al., 2006) usually rely on a cascaded approach by first translating speech into text in the target language, either with automatic speech recognition (ASR) followed by machine tranlsation (MT), or an end-to-end speech-to-text translation…”
• Direct Speech-to-Speech Translation With Discrete Units (2021, methodology): “Conventional S2ST systems are built by combining either cascaded or end-to-end S2T models with TTS [13, 14].”
• Direct speech-to-speech translation with a sequence-to-sequence model (2019, neutral): “This is in contrast to conventional S2ST systems which are often broken down into three components: automatic speech recognition (ASR), text-to-text machine translation (MT), and text-to-speech (TTS) synthesis [1–4].”
• UWSpeech: Speech to Speech Translation for Unwritten Languages (2020, background): “Existing speech to speech translation systems either rely on target text as a pivot (they first translate source speech into target text and then synthesize target speech given the translated text [19, 26, 42]), or directly translate source speech into target speech [15].”
• Speech-Centric Information Processing: An Optimization-Oriented Approach (2013, background): “While the initial crude mathematical formulation of this approach appeared in the early days of SLT research [66] and later extended to joint ASR and MT decoding through an ASR lattice or confusion network [8], [96], only at the decoding stage has the ASR uncertainty been considered until rather…”
• Transformer-Based Direct Speech-To-Speech Translation with Transcoder (2021, background): “The traditional speech translation system follows this step-by-step process that connect ASR, MT, and TTS to translate speech to another language’s speech [1].”
• Translatotron 2: High-quality direct speech-to-speech translation with voice preservation (2021, methodology): “Related works S2ST Until very recently, automatic S2ST systems are typically composed of a cascade of ASR, MT, and TTS components (Lavie et al., 1997; Wahlster, 2000; Nakamura et al., 2006; ITU, 2016).”
• SpeechMatrix: A Large-Scale Mined Corpus of Multilingual Speech-to-Speech Translations (2022, background): “matic speech recognition (ASR), machine translation (MT) and text-to-speech synthesis (TTS) (Nakamura et al., 2006; Do et al., 2015).”
• Joint Pre-Training with Speech and Bilingual Text for Direct Speech to Speech Translation (2022, unknown): “Conventional speech to speech translation is usually composed of cascaded ASR, MT and TTS modules [16, 17].”
• TranSpeech: Speech-to-Speech Translation With Bilateral Perturbation (2022, methodology): “Among the conventional method (Lavie et al., 1997; Nakamura et al., 2006; Wahlster, 2013), the cascaded system of automatic speech recognition (ASR), machine translation (MT), or speech-to-text translation (S2T) followed by text-to-speech synthesis (TTS) have demonstrated reasonable results yet…”
• Translatotron 2: Robust direct speech-to-speech translation (2021, background): “Conventional S2ST systems are often composed of a cascade of three components: automatic speech recognition (ASR), text-to-text machine translation (MT), and text-to-speech (TTS) synthesis (Lavie et al., 1997; Wahlster, 2000; Nakamura et al., 2006; International Telecommunication Union, 2016).”