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Modern speech synthesis: communication aid personalisation

Modern speech synthesis: communication aid personalisation. Sarah Creer Stuart Cunningham Phil Green Clinical Applications of Speech Technology University of Sheffield. Introduction. Building voices for VIVOCA (communication aids) Speech synthesis techniques

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Modern speech synthesis: communication aid personalisation

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  1. Modern speech synthesis: communication aid personalisation Sarah Creer Stuart Cunningham Phil Green Clinical Applications of Speech Technology University of Sheffield

  2. Introduction • Building voices for VIVOCA (communication aids) • Speech synthesis techniques • Future research: personalisation of synthetic voices

  3. Current speech synthesis: communication aids • High quality voices available • E.g. Toby Churchill Lightwriter • DECtalk™ (Fonix) for American English • Acapela for British English • Personalisation limited: age, gender, language

  4. Personalisation • Voice = identity • Gender • Age • Geographic background • Socio-economic background • Ethnic background • As that individual • Maintains social relationships • Maintains social closeness • Sets group membership

  5. VIVOCA • Voice Input Voice Output Communication Aid Dysarthric speech input Recognised text Speech Recognition Text-to-Speech Synthesis Intelligible and personalised synthesised speech output Intelligiblesynthesised speech output • Retain elements of clients’ identity for • synthesised speech output

  6. VIVOCA: personalisation • Sheffield/Barnsley user group • Retain local accent • geographic identity • Speaker database • Arctic database: 593 + 20 sentences • Professional local speakers • Ian McMillan • Christa Ackroyd

  7. Concatenative synthesis Festvox: http://festvox.org/ Speech recordings Unit segmentation Input data i a sh Unit database Text input Synthesised speech Unit selection Concatenation + smoothing … + + + …

  8. Concatenative synthesis • High quality • Natural sounding • Sounds like original speaker • Need a lot of data (~600 sentences) • Can be inconsistent • Difficult to manipulate prosody

  9. HMM synthesis y e s yes

  10. HMM synthesis procedure HTS http://hts.sp.nitech.ac.jp/ Speech recordings Text input Input data e Training Synthesis t Synthesised speech Speaker model

  11. HMM synthesis • Consistent • Intelligible • Needs relatively little input (~20 mins) • Can be adapted with small amount of data (>5 sentences) • Easier to manipulate • Buzzy quality • Less natural than concatenative

  12. Future research • Further personalisation for individuals with progressive speech disorders • Capturing the essence of a voice • Voice banking • Before deterioration • Adaptation using HMM synthesis • Before or during deterioration

  13. Thank you This work is sponsored by EPSRC Doctoral Training grant For further details of VIVOCA see: http://www.shef.ac.uk/cast/ Email: S.Creer@dcs.shef.ac.uk

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