The BBC s Virtual Voice-over tool ALTO: Technology for Video Translation
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1 The BBC s Virtual Voice-over tool ALTO: Technology for Video Translation Susanne Weber Language Technology Producer, BBC News Labs
2 In this presentation. - Overview over the ALTO Pilot project - Machine Translation and Computer Assisted Translation - Text to Speech synthesis - Users experience with this technology - Conclusions
3 Production tool for the translation of News videos Collaboration between - News Labs - World Service - Global News
4 Go to And
5 We experimented with 2 types of News Videos - Short clips without original narrator track - News Packages containing several voices
6 How do we currently translate videos?
7 Typical Workflow for Video Translation Translate Script Record Voice-over tracks Align Audio & Video Edit Audio Balance Audio Tracks
8
9 Off-the-shelf products
10 Computer-Assisted Translation
11 Computer-Assisted Translation How Good Is it???
12 To put things into perspective - ca. 7,000 languages in the world - Google Translate lists just over 100 languages - Most TTS providers have fewer than 30 languages
13 Machine Translation Computer Assisted Translation High Resourced vs. Low Resourced Languages MT quality depends on: Language Pairs Source Text Our editors feedback: - CAT is still faster than translating from scratch - CAT is useful for proof-reading
14
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16 It is difficult to get good quality voices why is that? Currently, we are dependent on a small number of companies Why do some of them sound so natural, others don t? Why can t we have them in all the languages?
17 There are 2 common methods for voices synthesis: 1) Unit Selection 2) Statistical Parametric
18 Creating synthetic voices: Unit Selection Record Voice Pron Lexicon and word labels Scripts (phonemes etc) to generate utterances data: blah blah Utterance files
19 Text-To-Speech Synthesis: Unit Selection Utterance files Overlap / crossfade Input text NLP: Produce linguistic specification Select phonemes Concatenate waveforms Pron Lexicon Prosody, stress, duration Output (spoken text)
20 Unit Selection Audio Examples Japanese:
21 Unit Selection User Feedback - It sounds surprisingly natural what is natural? There is no objective measurement of naturalness it is subjective are accents natural? Scottish? Welsh? when they are human-like = natural
22 Unit Selection Limitations - TTS voices are emotionally neutral - This is good for regular news - Unsuitable for emotionally charged contents, e.g. when voicing over victims of bomb attacks - We have no control over their emotional expression in Unit Selection
23 Unit Selection Phonetic performance control / Limitations Spelling Audio (English, UK) Angela Merkel Ang ella Markel Vladimir Putin Vladimeer Pootin Francois Hollande Francois O Lond Pros / cons
24 Training of Models: Statistical Parametric (simplified) Speech Database Text / Words: LABELS Speech Signal Excitation Parameter Extraction Training of TTS models Spectral Parameter Extraction Hidden Markov Models
25 Voice Synthesis: Statistical Parametric (simplified) Hidden Markov Models Input text Convert into Label Sequence Construct Utterances by concatenating Hidden Markov models Context dependent Generate Excitation Synthesized Speech Generate Spectral Parameter
26 Statistical parametric TTS the good bits - It is flexible, because of its statistical modelling process - It allows expressive voices to be generated; - the emotional expression of voices can be controlled - Voices are easier to build, because it doesn t need large amounts of datasets - this is good for low-resourced languages
27 Statistical parametric TTS the sound Audio examples: Unit Selection Japanese HMM Japanese Please go to this link:
28 Conclusion and Next Steps: We need language data for low resourced languages: For MT as well as TTS We need more languages and voices to be available We need expressive voices (e.g. a hybrid system) Collaborate with research groups and universities We want to tackle Graphics Translation And integrate automated transcription
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