The EMIME Bilingual Database

By Mirjam Wester. 2010

51 citations recorded by the source corpus.

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Citations in the corpus

VioLA: Unified Codec Language Models for Speech Recognition, Synthesis, and Translation (2023, neutral): “The Chinese-to-English S2TT (ZH ⇒ EN S2TT), zero-shot English TTS prompted by Chinese speech (ZH (cid:55)→ EN TTS), and zero-short Chinese-to-English S2ST (ZH ⇒ EN S2ST) are evaluated on the EMIME dataset.”
Speak Foreign Languages with Your Own Voice: Cross-Lingual Neural Codec Language Modeling (2023, neutral): “We evaluate zero-shot S2ST using the Effective Multilingual Interaction in Mobile Environments (EMIME) dataset [Wester, 2010], which contains bilingual Chinese/English speech recorded by the same speakers.”
LM-VC: Zero-Shot Voice Conversion via Speech Generation Based on Language Models (2023, methodology): “For zero-shot testing, a set of 500 testing pairs is selected from VCTK [32], CMU Arctic [33], and EMIME [34], each with a source and target speaker utterance.”
A DNN-based Mandarin-Tibetan cross-lingual speech synthesis (2018, methodology): “We select seven female speakers’ recordings (169 sentences per person) from EMIME bi-lingual speech database [24] as Mandarin corpus, and record 800 Tibetan speech of a native female Tibetan Lhasa dialect speaker as Tibetan training corpus.”
The EMIME Mandarin Bilingual Database (2011, methodology): “(for more details on the German and Finnish data see [1]) Each test condition consists of 84 trials: 14 talkers x 3 sentences x 2.”
Current trends in multilingual speech processing (2011, background): “We also showed that the language of the reference speech plays an important role in people’s ability to evaluate speaker similarity, a topic which is now under investigation (Wester et al 2010; Wester 2010a,b).”