In this paper, we develop a computationally efficient approach for warp factor estimation in Vocal Tract Length Normalization (VTLN). Recently we have shown that warped features can be obtained by a linear transformation of the unwarped features. Using the warp matrices we show that warp factor estimation can be efficiently performed in an EM framework. This can be done by collecting Sufficient Statistics by aligning the unwarped utterances only once. The likelihood of warped features, which are necessary for warp factor estimation, are computed by appropriately modifying the sufficient statistics using the warp matrices. We show using OGI, TIDIGITS and RM task that this approach has recognition performance that is comparable to conventional VTLN and yet is computationally more efficient.

A computationally efficient approach to warp factor estimation in VTLN using EM algorithm and sufficient statistics.

Rapidly adapting a speech recognition system to new speakers using a small amount of adaptation data is important to improve initial user experience. In this paper, a count-smoothing framework for incorporating prior information is extended to allow for the use of different forms of dynamic prior and improve the robustness of transform estimation on small amounts of data. Prior information is obtained from existing rapid adaptation techniques like VTLN and PCMLLR. Results using VTLN as a dynamic prior for CMLLR estimation show that transforms estimated on just one utterance can yield relative gains of 15% and 46% over a baseline gender independent model on two tasks. Index Terms: automatic speech recognition, speaker adaptation, VTLN, prior knowledge

/pdf/prior-information-for-rapid-speaker-adaptation-1h3w71ubuf.pdf

Prior Information for Rapid Speaker Adaptation

Vocal tract length normalization (VTLN) has been successfully used in automatic speech recognition for improved performance. The same technique can be implemented in statistical parametric speech synthesis for rapid speaker adaptation during synthesis. This paper presents an efficient implementation of VTLN using expectation maximization and addresses the key challenges faced in implementing VTLN for synthesis. Jacobian normalization, high-dimensionality features and truncation of the transformation matrix are a few challenges presented with the appropriate solutions. Detailed evaluations are performed to estimate the most suitable technique for using VTLN in speech synthesis. Evaluating VTLN in the framework of speech synthesis is also not an easy task since the technique does not work equally well for all speakers. Speakers have been selected based on different objective and subjective criteria to demonstrate the difference between systems. The best method for implementing VTLN is confirmed to be use of the lower order features for estimating warping factors.

/pdf/vocal-tract-length-normalization-for-statistical-parametric-3isguimvy2.pdf

Vocal Tract Length Normalization for Statistical Parametric Speech Synthesis

In this paper, we propose a method to analytically obtain a linear-transformation on the conventional Mel frequency cepstral coefficients (MFCC) features that corresponds to conventional vocal tract length normalization (VTLN)-warped MFCC features, thereby simplifying the VTLN processing. There have been many attempts to obtain such a linear-transformation, but all the previously proposed approaches either modify the signal processing (and therefore not conventional MFCC), or the linear-transformation does not correspond to conventional VTLN-warping, or the matrices being estimated and are data dependent. In short, the conventional VTLN part of an automatic speech recognition (ASR) system cannot be simply replaced with any of the previously proposed methods. Umesh proposed the idea to use band-limited interpolation for performing VTLN-warping on MFCC using plain cepstra. Motivated from this work, Panchapagesan and Alwan proposed a linear-transformation to perform VTLN-warping on conventional MFCC. However, in their approach, VTLN warping is specified in the Mel-frequency domain and is not equivalent to conventional VTLN. In this paper, we present an approach which also draws inspiration from the work of Umesh , and which we believe for the first time performs conventional VTLN as a linear-transformation on conventional MFCC using the ideas of band-limited interpolation. Deriving such a linear-transformation to perform VTLN, would allow us to use the VTLN-matrices in transform-based adaptation framework with its associated advantages and yet would require the estimation of a single parameter. Using four different tasks, we show that our proposed approach has almost identical recognition performance to conventional VTLN on both clean and noisy speech data.

VTLN Using Analytically Determined Linear-Transformation on Conventional MFCC

Vocal tract length normalization is an important feature normalization technique that can be used to perform speaker adaptation when very little adaptation data is available. It was shown earlier that VTLN can be applied to statistical speech synthesis and was shown to give additive improvements to CMLLR. This paper presents an EM optimization for estimating more accurate warping factors. The EM formulation helps to embed the feature normalization in the HMM training. This helps in estimating the warping factors more efficiently and enables the use of multiple (appropriate) warping factors for different state clusters of the same speaker.

http://publications.idiap.ch/downloads/papers/2010/Saheer_ISCASPEECHSYNTHESISWORKSHOP(SSW7)_2010.pdf

Implementation of vtln for statistical speech synthesis

In this paper, we present a linear transformation (LT) to obtain warped features from unwarped features during vocal-tract length normalisation (VTLN). This LT between the warped and unwarped features is obtained within the conventional MFCC framework without any modification in the signal processing steps involved during the feature extraction stage. Further using the proposed LT, we study the effect of the Jacobian on the VTLN performance and show that it provides additional improvement in the recognition performance. The Jacobian of the proposed LT is simply the determinant of the LT matrix. Jacobian compensation is not done in conventional VTLN as the relation between warped and unwarped features is not known. We also study the effect of cepstral variance normalisation (CVN), which is often used as an approximation for Jacobian compensation in conventional VTLN. We show that the proposed Jacobian compensation gives better or comparable performance when compared to CVN.

Study of jacobian compensation using linear transformation of conventional MFCC for VTLN.

In the paper, we present a novel linear transformation approach to frequency warping during vocal tract length normalisation(VTLN) using the idea of dynamic frequency warping(DFW). Linear transformation among the mel-frequency cepstral coefficients (MFCC) provides computational advantage of not having to recompute features for each warp factor in VTLN. The proposed method uses the idea of separating the smoothing and the frequency warping operations in the feature extraction stage unlike the conventional approach where both operations are integrated into the filter-bank operation. The advantage of the proposed DFW approach is that, we can obtain a transformation matrix for any arbitrary warping even when we do not know the functional form or mapping of the warping function. We compare the performance of the proposed method along with approaches proposed in [4] and [5] on one phone classification and two digit recognition tasks.

Linear transformation approach to VTLN using dynamic frequency warping.

In this paper, we first show that accounting for Jacobian in Vocal-Tract Length Normalization (VTLN) will degrade the performance when there is a mismatch between the train and test speaker conditions. VTLN is implemented using our recently proposed approach of linear transformation of conventional MFCC, i.e. a feature transformation. In this case, Jacobian is simply the determinant of the linear transformation. Feature transformation is equivalent to the means and covariances of the model being transformed by the inverse transformation while leaving the data unchanged. Using a set of adaptation experiments, we analyze the reasons for the degradation during Jacobian compensation and conclude that applying the same VTLN transformation on both means and variances does not fully match the data when there is a mismatch in the speaker conditions. This may have similar implications for constrainedMLLR in mismatched speaker conditions. We then propose to use covariance adaptation on top of VTLN to account for the covariance mismatch between the train and the test speakers and show that accounting for Jacobian after covariance adaptation improves the performance.

A study on the influence of covariance adaptation on jacobian compensation in vocal tract length normalization.

In this paper, we present a study to understand the relation among spectra of speakers enunciating the same sound and investigate the issue of uniform versus non-uniform scaling. There is a lot of interest in understanding this relation as speaker variability is a major source of concern in many applications including Automatic Speech Recognition (ASR). Using dynamic programming, we find mapping relations between smoothed spectral envelopes of speakers enunciating the same sound and show that these relations are not linear but have a consistent non-uniform behavior. This non-uniform behavior is also shown to vary across vowels. Through a series of experiments, we show that using the observed non-uniform relation provides better vowel normalization than just a simple linear scaling relation. All results in this paper are based on vowel data from TIMIT, Hillenbrand et al. and North Texas databases.

D. Rama Sanand

Papers

A computationally efficient approach to warp factor estimation in VTLN using EM algorithm and sufficient statistics.

Study of jacobian compensation using linear transformation of conventional MFCC for VTLN.

Linear transformation approach to VTLN using dynamic frequency warping.

A study on the influence of covariance adaptation on jacobian compensation in vocal tract length normalization.

Characterizing speaker variability using spectral envelopes of vowel sounds.