Verbmobil: Foundations of Speech-to-Speech Translation

By W. Wahlster. 2000

335 citations recorded by the source corpus.

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In Artificial Intelligence

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].”
PolyVoice: Language Models for Speech to Speech Translation (2023, methodology): “Speech-to-speech translation (Lavie et al., 1997; Baldridge, 2004; Nakamura et al., 2006) aims to develop models capable of generating target language speech from source language speech.”
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.”
UWSpeech: Speech to Speech Translation for Unwritten Languages (2020, background): “Speech to speech translation (Lavie et al. 1997; Nakamura et al. 2006; Wahlster 2013; Jia et al. 2019) is important to help the understanding of cross-lingual spoken conversations and lectures, and has been used in scenarios such as international travel or conference.”
The Alignment Template Approach to Statistical Machine Translation (2004, background): “domain of appointment scheduling, travel planning, and hotel reservation (Wahlster 2000).”
Sign-Based Construction Grammar: An Informal Synopsis (2012, background): “By contrast , Fillmore ( 1999 ) argued ( 1 ) that there were no such grammatically encoded properties , ( 2 ) that individual SAI - subconstructions specify their own semantic properties , and hence ( 3 ) that constructions need not involve semantics .”
Conversational Agents and Natural Language Interaction: Techniques and Effective Practices (2011, background): “There is a significant number of practical (Dahl, 2004) and more advanced (Pellom et al., 2001; Allen et al., 1995, 1996, 2001; Wahlster, 2000; Sidner, 2004, among others) system descriptions.”
The ATR Multilingual Speech-to-Speech Translation System (2006, methodology): “Many research projects have addressed speech-to-speech translation technology, such as VERB-MOBIL [ 1 ], C-STAR,1 NESPOLE!,2 [2], and BABYLON.3 These projects mainly focused on the construction of prototype systems for several language pairs.”
Databases, features and classifiers for speech emotion recognition: a review (2018, background): “It includes the data recorded from call center conversations, cockpit recordings during abnormal conditions, conversation between a patient and a doctor, conversation with emotions in public places and similar interactions Batliner et al. (2000).”
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…”
Accuracy vs. Robustness in Grammar Engineering (2010, methodology): “The VerbMobil project (Wahlster, 2000) developed, among its many results, a collection of transcriptions of spoken dialogues each of which reflected a negotiation either to schedule a meeting, or to plan a hotel stay.”
Out-of-Domain Utterance Detection Using Classification Confidences of Multiple Topics (2007, background): “876727 information access [1]–[4], speech-based call-routing systems [5]–[7] and, more recently, speech-to-speech translation systems [8]–[10].”
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).”
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).”
Understanding the societal impacts of machine translation: a critical review of the literature on medical and legal use cases (2020, background): “Some of these are not dissimilar from earlier MT-based conversational systems (see e.g. Wahlster, 2000).”