IBM slide textures for black backgrounds

MIME-Version: 1.0 Content-Type: multipart/related; boundary="----=_NextPart_01C6CDFD.83CA21C0" This document is a Single File Web Page, also known as a Web Archive file. If you are seeing this message, your browser or editor doesn't support Web Archive files. 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"Please view t= hese slides in..."

Empowering Text-to-= Speech

Basic Text-to-Speech Quality

Limitations of Basic System

"Record databa= ses in each of..."

Voice Conversion � Overview of Approach

Harmonic Sinusoidal= Model of Voiced Speech

Voice Conversion Fr= om Sinusoidal Representation

Voice Conversion Fr= om Sinusoidal Representation

Voice Conversion � Example

TTS Customization

TTS Customization -- Examples

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"Please view t= hese slides in..."

Please view these s= lides in screen show mode, as they contain animation.

Empowering Text-to-= Speech

Ellen Eide, Andy Aa= ron, Raimo Bakis, Raul Fernandez, Wael Hamza, Michael Picheny,� Zhi Wei Shuang*

IBM TJ Watson Resea= rch Center��= �� * IBM China Research Lab

Yorktown Heights, N= Y USA��= �� Beijing, China

Basic Text-to-Speech Quality

Quality is text-dep= endent and speaker-dependent

Live demo��= �� www.research.ibm.com/tts

Limitations of Basic System

Produces newsy, sli= ghtly perky speech in the
speaker�s voice

Inappropriate for v= ery good or very bad news

Solution: expressiv= e TTS

Cannot satisfy cust= omer�s desired voice characteristics

Solution: voice con= version

"Record databa= ses in each of..."

Record databases in= each of a set of expressive styles

Synthesize speech u= sing the appropriate database

Voice Conversion � Overview of Approach

Multidimensional pr= oblem encompassing pitch, vocal tract resonances, accent, prosody, speaking rat= e, vocabulary, �

Requires the abilit= y to adjust the effect of the pitch and vocal tract (formants) independently.<= /td>

Simple time scaling= is insufficient; pitch and formants are uniformly scaled

Use a decomposition= based on sinusoidal analysis to separate pitch and spectral envelope

Works best when sou= rce and target are similar

Suggests having a s= et of source voices which cover the acoustic space � chose closest to target as a star= ting point

Harmonic Sinusoidal= Model of Voiced Speech

Voice Conversion Fr= om Sinusoidal Representation

Voice Conversion Fr= om Sinusoidal Representation

We could also re-sa= mple the original envelope at a new pitch

Voice Conversion � Example

Original Speaker

TTS Customization

Augment� a corpora of customer prompts with off-the-shelf� voice data<= /td>

Off-the-shelf data = can be optionally morphed to better match customer voice

Use the expressive = framework approach for synthesis, treating speakers as styles

Tune cost matrix to= ensure that output speech sounds like target speaker

TTS Customization -- Examples

Target� (40 minutes of speech)

Summary

Baseline TTS can ac= hieve high quality speech, but has limitations:

One-size-fits-all e= xpression

Fixed voice<= /td>

Added expressivenes= s by recording (small) expressive databases

Added voice convers= ion making use of a sinusoidal model representation

Expressive framewor= k + voice conversion can be used for augmenting a custom dataset

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