Overview Of DSP

16-Text To Speech (TTS)

Speech SynthesisSpeech Synthesis Concept Speech NaturalnessPhone Sequence To SpeechArticulatory ApproachesConcatenative ApproachesHMM-based ApproachesRule-Based Approaches

12Speech Synthesis ConceptText toPhone SequencePhone Sequenceto SpeechTextSpeech waveformNatural Language Processing (NLP)Speech ProcessingTextSpeech waveform23Speech NaturalnessObviation of undesirable noise and distortion and dissociation from speechProsody generationSpeech energyDurationpitchIntonationStress

34Speech Naturalness (Contd)Intonation and Stress are very effective in speech naturalness

Intonation : Variation of Pitch frequency along speakingStress : Increasing the pitch frequency in a specific time

4Which word receives an intonation?It depends on the context. The new information in the answer to a question is often accentedwhile the old information is usually not.

Q1: What types of foods are a good source of vitamins?A1: LEGUMES are a good source of vitamins.

Q2: Are legumes a source of vitamins?A2: Legumes are a GOOD source of vitamins.

Q3: Ive heard that legumes are healthy, but what are they a good source of ?A3: Legumes are a good source of VITAMINS.

Slide from Jennifer Venditti5Same tune, different alignment

LEGUMES are a good source of vitamins

The main rise-fall accent (= I assert this) shifts locations.Slide from Jennifer Venditti6Same tune, different alignment

Legumes are a GOOD source of vitamins

The main rise-fall accent (= I assert this) shifts locations.Slide from Jennifer Venditti7Same tune, different alignmentlegumes are a good source of VITAMINS

The main rise-fall accent (= I assert this) shifts locations.Slide from Jennifer Venditti8Types of Waveform Synthesis9Articulatory Synthesis:Model movements of articulators and acoustics of vocal tractConcatenative Synthesis:Use databases of stored speech to assemble new utterances.DiphoneUnit SelectionStatistical (HMM) SynthesisTrains parameters on databases of speechRule-Based (Formant) Synthesis:Start with acoustics, create rules/filters to create waveform

Articulatory SynthesisSimulation of physical processes of human articulation Wolfgang von Kempelen (1734-1804) and others used bellows, reeds and tubes to construct mechanical speaking machinesModern versions simulate electronically the effect of articulator positions, vocal tract shape, etc on air flow.10Concatenative approachesTwo main approaches:1- Concatenating Phone UnitsExample: concatenating samples of recorded diphones or syllables2- Unit selectionUses several samples for each phone unit and selects the most appropriate one when synthesizing 1112Phone UnitsParagraph ( )

Sentence ( )

Word (Depends on the language. Usually more than 100,000)

Syllable

Diphone & Triphone

Phoneme (Between 10 , 100)

1213Phone Units (Contd)Diphone : We model Transitions between two phonemesp1p2p3p4p5. . . . .DiphonePhoneme1314Phone Units (Contd)Farsi phonemes: 30Farsi diphones: 30*30 = 900 Phoneme /zho/ is missing (?)Farsi triphones: 27000 in theory Not all of the triphones are used 1415Phone Units (Contd)Syllable = Onset (Consonant) + RhymeSyllable is a set of phonemes that exactly contains one vowelSyllables in Farsi : CV , CVC , CVCC We have about 4000 Syllables in FarsiSyllables in English :V, CV , CVC ,CCVC, CCVCC, CCCVC, CCCVCC, . . .Number of Syllables in English is too many1516Phone Sequence To Speech (Contd)Text to Phone SequencePhone Sequence to primitive utteranceTextSpeechprimitive utteranceto NaturalSpeechNLPSpeech Processing1617Concatenative ApproachesIn this approaches we store units of natural speech for reconstruction of desired speechWe could select the appropriate phone unit for speech synthesiswe can store compressed parameters instead of main waveform

1718Concatenative Approaches (Contd)Benefits of storing compressed parameters instead of main waveformLess memory useGeneral state instead of a specific stored utteranceGenerating prosody easily1819Concatenative Approaches (Contd)Phone UnitType of StoringParagraph

Sentence

Word

Syllable

Diphone

PhonemeMain Waveform

Main Waveform

Main Waveform

Coded/Main Waveform

Coded Waveform

Coded Waveform1920Concatenative Approaches (Contd)Pitch Synchronous Overlap-Add-Method (PSOLA) is a famous method in phoneme transmit smoothing

Overlap-Add-Method is a standard DSP method

PSOLA is a base action for Voice Conversion.

In this method in analysis stage we select frames that are synchronous by pitch markers.20Diphone Architecture ExampleTraining:Choose units (kinds of diphones)Record 1 speaker saying 1 example of each diphoneMark the boundaries of each diphones, cut each diphone out and create a diphone databaseSynthesizing an utterance, grab relevant sequence of diphones from databaseConcatenate the diphones, doing slight signal processing at boundariesuse signal processing to change the prosody (F0, energy, duration) of selected sequence of diphones

21Unit SelectionSame idea as concatenative synthesis, but database contains bigger varieties of phone units from diphones to sentencesMultiple examples of phone units (under different prosodic conditions) are recordedSelection of appropriate unit therefore becomes more complex, as there are in the database competing candidates for selection22Unit Selection23Unlike diphone concatenation, little or no signal processing applied to each unitNatural data solves problems with diphonesDiphone databases are carefully designed but:Speaker makes errorsSpeaker doesnt speak intended dialectRequire database design to be rightIf its automaticLabeled with what the speaker actually saidCoarticulation, schwas, flaps are naturalTheres no data like more dataLots of copies of each unit mean you can choose just the right one for the contextLarger units mean you can capture wider effectsUnit Selection Issues24Given a big databaseFor each segment (diphone) that we want to synthesizeFind the unit in the database that is the best to synthesize this target segmentWhat does best mean?Target cost: Closest match to the target description, in terms ofPhonetic contextF0, stress, phrase positionJoin cost: Best join with neighboring unitsMatching formants + other spectral characteristicsMatching energyMatching F0

Unit Selection Search25

Joining Units26unit selection, just like diphone, need to join the unitsPitch-synchronouslyFor diphone synthesis, need to modify F0 and durationFor unit selection, in principle also need to modify F0 and duration of selection unitsBut in practice, if unit-selection database is big enough (commercial systems) no prosodic modifications (selected targets may already be close to desired prosody)Unit Selection Summary27AdvantagesQuality is far superior to diphonesNatural prosody selection sounds betterDisadvantages:Quality can be very bad in some placesHCI problem: mix of very good and very bad is quite annoyingSynthesis is computationally expensiveNeeds more memory than diphone synthesis28Rule-Based Approach StagesDetermine the speech model and model parametersDetermine type of phone unitsDetermine some parameter amount for each phone unitSubstitute sequence of phone units by its equivalent parameter sequencePut parameter sequence in speech model2829KLATT 80 Model

2930KLATT 88 Model

3031KL GLOTT 88 model (default)SPECTRAL TILT LOW-PAS RESONANTORMODIFIED LF MODELASPIRATION NOISE GENERATORFIRST DIFFERENCE PREEMPHASISNASAL FORMANT RESONATORTRACHEAL FORMANT RESONATORFOURTH FORMANT RESONATORTHIRTH FORMANT RESONATORSECOND FORMANT RESONATORFIRST FORMANT RESONATORFRICATION NOISE GENERATORSECOND FORMANT RESONATORTHIRD FORMANT RESONATORFOURTH FORMANT RESONATORFIFTH FORMANT RESONATORSIXTH FORMANT RESONATORA2FA3FA4FA5FA6FABANVA1VA2VA3VA4VATV+-+-+-++-+--++FILTERED IMPULSE TRAIN F0 AV OO FL DISOSSTLAH AFGLOTTAL SOUND SOURCESCPBYPASS PATH B2F B3F B4F B5F B6F F6PARALLEL VOCAL TRACT MODEL LYRYNGEALSOUND SOURCES (NORMALLY NOT USED) PARALLEL VOCAL TRACT MODEL FRICATION SOUND SOURCES BNP BNZ BTP BTZ DF1 DB1 F2 B2 F3 B3 F4 B4 F5 B5 CASCADE VOCAL TRACT MODEL LARYNGEAL SOUND SOURCESNASAL POLE ZERO PAIRTRACHEAL POLE ZERO PAIRFIRST FORMANT RESONATORSECOND FORMANT RESONATORTHIRTH FORMANT RESONATORFOURTH FORMANT RESONATORFIFTH FORMANT RESONATORTHE KLSYN88 CASCADE PARALLEL FORMANT SYNTHESIZERFNP FNZ FTP FTZ F1 B13132Three Voicing Source Model In KLATT 88The old KLSYN impulsive source

The KLGLOTT88 model

The modified LF model32HMM-Based SynthesisCorpus-based, statistical parametric synthesisProposed in mid-'90s, becomes popular since mid-'00s Large data + automatic training => Automatic voice buildingSource-filter model + statistical acoustic model Flexible to change its voice characteristicsHMM as its statistical acoustic modelWe focus on HMM-based speech synthesis33

34First extract parametric representations of speech including spectral and excitation parameters from a speech database Model them by using a set of generative models (e.g., HMMs)

Training

Synthesis

35

Speech Parameter Modeling Based on HMM

Spectral parameter modeling

Excitation parameter modeling

State duration modeling36

Spectral parameter modeling

Mel-cepstral analysis has been used for spectral estimation A continuous density HMM is used for the vocal tract modeling in the same way as speech recognition systems.The continuous density Markov model is a finite state machine which makes one state transition at each time unit (i.e. frame). First, a decision is made to which state to succeed (including the state itself). Then an output vector is generated according to the probability density function (pdf) for the current state

37Contd

38F0 parameter modelingWhile the observation of F0 has a continuous value in the voiced region, there exists no value for the unvoiced region. We can model this kind of observation sequence assuming that the observed F0 value occurs from one-dimensional spaces and the unvoiced symbol occurs from the zero-dimensional space.39

40Calculation of dynamic feature:

As was mentioned, mel-cepstral coefficient is used as spectral parameter, Their dynamic feature c and 2c are calculated as follows:

Dynamic features for F0: In unvoiced region, pt, pt and 2pt are defined as a discrete symbol. When dynamic features at the boundary between voiced and unvoiced cannot be calculated, they are defined as a discrete symbol.

41Effect of dynamic featureBy using dynamic features, the generated speech parameter vectors reflect not only the means of static and dynamic feature vectors but also the covariances of thoseEstimation will be smootherGood and bad effect

42Multi-Stream HMM structure:The sequence of mel-cepstral coefficient vector and F0 pattern are modeled by a continuous density HMM and multi-space probability distribution HMMPutting all this together has some advantages

43Synthesis partAn arbitrarily given text to be synthesized is converted to a context-based label sequence.The text is converted a context dependent label sequence by a text analyzer.For the TTS system, the text analyzer should have the ability to extract contextual information. However, no text analyzer has the ability to extract accentual phrase and to decide accent type of accentual phrase.

44Some contextual factors

When we have HMM for each phoneme{preceding, current, succeeding} phoneme position of breath group in sentence{preceding, current, succeeding} part-of-speech position of current accentual phrase in current breath groupposition of current phoneme in current accentual phrase45

46According to the obtained state durations, a sequence of mel-cepstral coefficients and F0 values including voiced/unvoiced decisions is generated from the sentence HMM by using the speech parameter generation algorithmFinally, speech is synthesized directly from the generated mel-cepstral coefficients and F0 values by the MLSA filter 47Spectral representation & corresponding filter48cepstrum: LMA filtergeneralized cepstrum: GLSA filtermel-cepstrum: MLSA (Mel Log Spectrum Approximation) filtermel-generalized cepstrum: MGLSA filterLSP: LSP filterPARCOR: all-pole lattice filterLPC: all-pole filterAdvantages

Most of the advantages of statistical parametric synthesis against unit-selection synthesis are related to its flexibility due to the statistical modeling process.Transforming voice characteristics, speaking styles, and emotions.Also Combination of unit selection and voice conversion (VC) techniques can alleviate this problem, high-quality voice-conversion is still problematic.49Adaptation (mimicking voices):Techniques of adaptation were originally developed in speech recognition to adjust general acoustic model , These techniques have also been applied to HMM-based speech synthesis to obtain speaker-specific synthesis systems with a small amount of speech data

Interpolation (mixing voices): Interpolate parameters among representative HMM sets - Can obtain new voices even no adaptation data is available - Gradually change speakers. & speaking styles 50

Some rule-based applicationsKLATT C Implementation:http://www.cs.cmu.edu/afs/cs/project/ai-repository/ai/areas/speech/systems/klatt/0.htmlA web page which generates speech waveform online given KLATT parameters:http://www.asel.udel.edu/speech/tutorials/synthesis/Klatt.htmlFormant Synthesis Demousing Fants Formant Modelhttp://www.speech.kth.se/wavesurfer/formant/

51

TTS Online Demos52AT&T:http://www2.research.att.com/~ttsweb/tts/demo.phpFestivalhttp://www.cstr.ed.ac.uk/projects/festival/morevoices.htmlCepstralhttp://www.cepstral.com/cgi-bin/demos/generalIBMhttp://www.research.ibm.com/tts/coredemo.shtml

FestivalOpen source speech synthesis systemDesigned for development and runtime useUse in many commercial and academic systemsDistributed with RedHat 9.x, etcHundreds of thousands of usersMultilingualNo built-in languageDesigned to allow addition of new languagesAdditional tools for rapid voice developmentStatistical learning toolsScripts for building models

1/5/07Text from Richard Sproat53Festival as softwarehttp://festvox.org/festival/General system for multi-lingual TTSC/C++ code with Scheme scripting languageGeneral replaceable modules:Lexicons, LTS, duration, intonation, phrasing, POS tagging, tokenizing, diphone/unit selection, signal processingGeneral toolsIntonation analysis (f0, Tilt), signal processing, CART building, N-gram, SCFG, WFST1/5/07Text from Richard Sproat54Festival as softwarehttp://festvox.org/festival/No fixed theoriesNew languages without new C++ codeMultiplatform (Unix/Windows)Full sources in distributionFree software1/5/07Text from Richard Sproat55CMU FestVox projectFestival is an engine, how do you make voices?Festvox: building synthetic voices:Tools, scripts, documentationDiscussion and examples for building voicesExample voice databasesStep by step walkthroughs of processesSupport for English and other languagesSupport for different waveform synthesis methodsDiphoneUnit selectionLimited domain1/5/07Text from Richard Sproat56Future TrendsSpeaker adaptationLanguage adaptation

57Chart9248.202230.644227.276226.373231.042235.713239.087251.727245.022249.882258.507269.58283.327294.118319.451343.743357.036346.464314.563329.45406.938402.141405.131404.805403.829399.91393.556386.56374.989345.9349.154341.028331.516323.055309.054299.166292.78277.072259.652241.007228.231215.976231.653269.282262.774254.843242.485235.654232.018230.149227.177226.369225.096222.779220.257216.445214.209212.298207.497201.984195.766193.418207.016197.942187.36183.172206.504219.758215.071207.319204.307204.69204.462202.198197.587202.766196.951185.566179.04243.13236.704221.931213.319206.919202.572201.498199.654197.934195.994192.289183.684193.533241.904224.76210.844205.573199.203193.096190.085188.068186.696186.512183.887184.178190.326195.455199.128197.176194.845192.673190.618189.428188.093187.543185.479184.29183.066181.507182.153176.603173.771184.76183.474180.751174.415176.679178.559177.772177.095175.801174.924174173.636173.578177.71166.567167.297

0.268953 0.300606 0.368337 0.31256 0.228394 0.942863 0.961095 0.994438 0.999319

plotF00.5543190.2689530.5453060.391640.3006060.3636760.3064850.3683370.4187880.3767210.312560.3305750.2005630.2283940.4366390.728813220.6230.9428630.4604470.596143243.5960.9610950.505831255.410.906279233.6160.9944380.46892261.0850.960008232.5620.9993190.598563251.6020.987756231.1270.998461248.2020.969884250.9180.997274233.7120.997134230.6440.996004251.7050.998068247.9160.888037227.2760.99519253.7550.999085242.5170.995132226.3730.99655264.7160.925374247.9780.994692231.0420.995347260.4990.993778252.7660.99367235.7130.997359262.9430.998183257.8130.99318239.0870.997097265.9490.997638263.5050.982483251.7270.928507267.9470.9847281.8130.900915245.0220.958637270.1710.935176287.920.982442249.8820.994234291.4930.969567283.4280.988984258.5070.992076266.8580.947052267.9770.993552269.580.981227245.40.992124268.5750.995162283.3270.982436249.0640.997975279.7960.986013294.1180.958114258.660.9377293.9890.992031319.4510.872069276.6850.961036308.2170.989433343.7430.957688293.8120.904206317.250.990976357.0360.990164305.1490.993789315.3670.994338346.4640.935607297.4840.98543313.3080.997944314.5630.970705302.970.993726311.7150.99511329.450.953978301.0840.994485308.9120.9953570.603465297.6360.995895297.9430.969620.570758291.7730.992789300.9160.999380.705848282.060.978445298.8140.9982770.676354285.8170.999891297.6970.999395406.9380.96696285.0430.999847296.3450.999869402.1410.997709284.1140.999197295.0690.999456405.1310.995769282.3540.999179292.5050.999153404.8050.996461278.5740.998367287.9790.997405403.8290.997439271.5250.996687284.9960.997182399.910.999687272.80.989086272.8530.99728393.5560.997623268.0350.996429264.6730.994204386.560.996774266.8590.99735264.4090.994769374.9890.995318273.5290.995008273.7790.99129345.90.946667284.9350.995305296.4050.98683349.1540.998778297.380.995266307.0390.997107341.0280.998963304.690.993139313.270.99675331.5160.997981306.6680.996686312.9310.995469323.0550.996673303.8170.993289310.2430.992325309.0540.997645300.8920.997757304.3530.992498299.1660.997304296.7560.996131299.6650.995322292.780.990872294.090.994828295.9110.999311277.0720.996129292.9640.994995295.5080.998072259.6520.993322290.6020.998619295.5310.998929241.0070.993166287.3110.996406296.1720.999012228.2310.98512285.4450.996301297.3680.998116215.9760.966576290.6160.984984298.4690.994626231.6530.929394289.0970.980074301.7150.997026269.2820.988117277.740.974877304.8320.989245262.7740.949345245.0030.9363304.8030.989481254.8430.98169246.8090.997563298.6920.98524242.4850.990562238.5840.994956256.2330.915484235.6540.992732232.010.987718255.5370.997549232.0180.9984310.389883252.7460.998769230.1490.992289287.8270.919862247.6750.996895227.1770.992221290.6030.989469244.5050.9917226.3690.995685290.9150.998512234.5680.983725225.0960.997521290.7430.9985670.787198222.7790.997463290.7550.9992760.79311220.2570.989438289.5280.9929220.379783216.4450.995367275.5070.9659850.300235214.2090.990622240.090.964914333.3470.96615212.2980.985166233.2470.979492346.5150.997893207.4970.977134220.1340.980413350.0240.99804201.9840.9836260.36863356.4440.999437195.7660.960870.80339365.5250.997541193.4180.9241760.558055375.7920.995303207.0160.9942480.312958384.9540.996535197.9420.9934440.398031377.9760.947096187.360.9858090.268754333.9770.961113183.1720.9938520.4873440.573889206.5040.8465190.2838750.605956219.7580.916984320.0920.857469377.8630.87437215.0710.986637305.2750.941476361.690.845786207.3190.992994298.2060.988170.701944204.3070.997895293.7110.9887920.455798204.690.999149288.3460.987490.443896204.4620.998051282.7360.9920520.251105202.1980.994589278.9110.9931290.400095197.5870.940176278.20.9919980.529991202.7660.991172275.1390.9959430.329884196.9510.993595277.1430.984275323.1720.86733185.5660.990683271.1140.972651330.8080.975348179.040.964844251.3980.931242341.0240.9965810.4571960.785792326.9950.9864750.4429160.432109298.1880.9437930.4625980.36577254.1990.9130960.4201120.373711244.5340.9918990.2834240.409862239.2830.9963770.5605320.338068234.9340.9968020.396640.415721230.2880.9901440.4816580.756303224.8850.9915260.430324297.0860.968552215.5920.9781020.766863290.9380.993428210.0930.917679243.130.897323284.9710.994355226.3710.971097236.7040.966928275.6630.993432199.1990.880513221.9310.959121262.3280.9927390.498454213.3190.984145247.9310.9910240.421676206.9190.987999241.8610.9947590.334139202.5720.994909237.6930.9911750201.4980.994079231.9560.9975470.323842199.6540.99734228.870.9977720.609052197.9340.993582227.2610.9956910.689923195.9940.987768227.8310.9990390.545383192.2890.985958229.3240.9971350.737755183.6840.935251241.7550.988532240.5460.888446193.5330.854188262.4550.956868222.8760.9799410.784909273.890.939576214.3790.9939420.385884275.6660.990228205.4610.99060.308879281.4090.995455196.2050.9765150.404036286.7390.994009189.0620.9849850.325016291.4530.994625189.5210.9880970.378393296.6510.99488188.170.9909850300.4280.993232188.2750.9938520.384959304.9120.98157186.1060.9974360.494912313.9870.984356186.990.9910240.729131325.4190.980719189.270.969113241.9040.819453332.518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plotF0

0.268953 0.300606 0.368337 0.31256 0.228394 0.942863 0.961095 0.994438 0.999319

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