Proceedings Fonetik 2009 - Institutionen för lingvistik

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Proceedings, FONETIK 2009, Dept. of Linguistics, Stockholm UniversityEstimating speaker characteristics for speech recognitionMats Blomberg and Daniel EleniusDept. of Speech, Music and Hearing, KTH/CSC, StockholmAbstractA speaker-characteristic-based hierarchic treeof speech recognition models is designed. Theleaves of the tree contain model sets, which arecreated by transforming a conventionallytrained set using leaf-specific speaker profilevectors. The non-leaf models are formed bymerging the models of their child nodes. Duringrecognition, a maximum likelihood criterionis followed to traverse the tree from theroot to a leaf. The computational load for estimatingone- (vocal tract length) and fourdimensionalspeaker profile vectors (vocal tractlength, two spectral slope parameters andmodel variance scaling) is reduced to a fractioncompared to that of an exhaustive searchamong all leaf nodes. Recognition experimentson children’s connected digits using adult modelsexhibit similar recognition performance forthe exhaustive and the one-dimensional treesearch. Further error reduction is achievedwith the four-dimensional tree. The estimatedspeaker properties are analyzed and discussed.IntroductionKnowledge on speech production can play animportant role in speech recognition by imposingconstraints on the structure of trained andadapted models. In contrast, current conventional,purely data-driven, speaker adaptationtechniques put little constraint on the models.This makes them sensitive to recognition errorsand they require a sufficiently high initial accuracyin order to improve the quality of the models.Several speaker characteristic propertieshave been proposed for this type of adaptation.The most commonly used is compensation formismatch in vocal tract length, performed byVocal Tract Length Normalization (VTLN)(Lee and Rose, 1998). Other candidates, lessexplored, are voice source quality, articulationclarity, speech rate, accent, emotion, etc.However, there are at least two problemsconnected to the approach. One is to establishthe quantitative relation between the propertyand its acoustic manifestation. The secondproblem is that the estimation of these featuresquickly becomes computationally heavy, sinceeach candidate value has to be evaluated in acomplete recognition procedure, and the numberof candidates needs to be sufficiently highin order to have the required precision of theestimate. This problem becomes particularlysevere if there is more than one property to bejointly optimized, since the number of evaluationpoints equals the product of the number ofindividual candidates for each property. Twostagetechniques, e.g. (Lee and Rose, 1998) and(Akhil et. al., 2008), reduce the computationalrequirements, unfortunately to the prize oflower recognition performance, especially if theaccuracy of the first recognition stage is low.In this work, we approach the problem ofexcessive computational load by representingthe range of the speaker profile vector as quantizedvalues in a multi-dimensional binary tree.Each node contains an individual value, or aninterval, of the profile vector and a correspondingmodel set. The standard exhaustive searchfor the best model among the leaf nodes cannow be replaced by a traversal of the tree fromthe root to a leaf. This results in a significantreduction of the computational amount.There is an important argument for structuringthe tree based on speaker characteristicproperties rather than on acoustic observations.If we know the acoustic effect of modifying acertain property of this kind, we can predictmodels of speaker profiles outside their rangein the adaptation corpus. This extrapolation isgenerally not possible with the standard acoustic-onlyrepresentation.In this report, we evaluate the predictionperformance by training the models on adultspeech and evaluating the recognition accuracyon children’s speech. The achieved results exhibita substantial reduction in computationalload while maintaining similar performance asan exhaustive grid search technique.In addition to the recognized identity, thespeaker properties are also estimated. As thesecan be represented in acoustic-phonetic terms,they are easier to interpret than the standardmodel parameters used in a recognizer. This154
Proceedings, FONETIK 2009, Dept. of Linguistics, Stockholm Universityprovides a mechanism for feedback fromspeech recognition research to speech productionknowledge.MethodTree generationThe tree is generated using a top-down designin the speaker profile domain, followed by abottom-up merging process in the acousticmodel domain. Initially, the root node is loadedwith the full, sorted, list of values for each dimensionin the speaker profile vector. A numberof child nodes are created, whose lists areachieved by binary splitting each dimension listin the mother node. This tree generation processproceeds until each dimension list has asingle value, which defines a leaf node. In thisnode, the dimension values define a uniquespeaker profile vector. This vector is used topredict a profile-specific model set by controllingthe transformation of a conventionallytrained original model set. When all child nodemodels to a certain mother node are created,they are merged into a model set at their mothernode. The merging procedure is repeated upwardsin the tree, until the root model isreached. Each node in the tree now contains amodel set which is defined by its list of speakerprofile values. All models in the tree have equalstructure and number of parameters.Search procedureDuring recognition of an utterance, the tree isused to select the speaker profile whose modelset maximizes the score of the utterance. Therecognition procedure starts by evaluating thechild nodes of the root. The maximumlikelihoodscoring child node is selected forfurther search. This is repeated until a stop criterionis met, which can be that the leaf level ora specified intermediate level is reached. Anotherselection criterion may be the maximumscoring node along the selected root-to-leafpath (path-max). This would account for thepossibility that the nodes close to the leaves fitpartial properties of a test speaker well but haveto be combined with sibling nodes to give anoverall good match.Model transformationsWe have selected a number of speaker propertiesto evaluate our multi-dimensional estimationapproach. The current set contains a fewbasic properties described below. These aresimilar, although not identical, to our work in(Blomberg and Elenius, 2008). Further developmentof the set will be addressed in futurework.VTLNAn obvious candidate as one element in thespeaker profile vector is Vocal Tract LengthNormalisation (VTLN). In this work, a standardtwo-segment piece-wise linear warping functionprojects the original model spectrum intoits warped spectrum. The procedure can be performedefficiently as a matrix multiplication inthe standard acoustic representation in currentspeech recognition systems, MFCC (Mel FrequencyCepstral Coefficients), as shown by Pitzand Ney (2005).Spectral slopeOur main intention with this feature is to compensatefor differences in the voice sourcespectrum. However, since the operation currentlyis performed on all models, also unvoicedand non-speech models will be affected.The feature will, thus, perform an overall compensationof mismatch in spectral slope,whether caused by voice source or the transmissionchannel.We use a first order low-pass function toapproximate the gross spectral shape of thevoice source function. This corresponds to theeffect of the parameter T a in the LF voicesource model (Fant, Liljencrants and Lin,1985). In order to correctly modify a model inthis feature, it is necessary to remove the characteristicsof the training data and to insertthose of the test speaker. A transformation ofthis feature thus involves two parameters: aninverse filter for the training data and a filterfor the test speaker.This two-stage normalization techniquegives us the theoretically attractive possibilityto use separate transformations for the vocaltract transfer function and the voice sourcespectrum (at least in these parameters). Afterthe inverse filter, there remains (in theory) onlythe vocal tract transfer function. Performingfrequency warping at this position in the chainwill thus not affect the original voice source ofthe model. The new source characteristics areinserted after the warping and are also unaffected.In contrast, conventional VTLN implicitlywarps the voice source spectrum identically155
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