A Bandpass Transform for Speaker Normalization

One of the major challenges for Automatic Speech Recognition is to handle speech variability. Inter-speaker variability is partly due to differences in speakers' anatomy and especially in their Vocal Tract geometry. Dissimilarities in Vocal Tract Length (VTL) are a known source of speech variation. Vocal Tract Length Normalization is a popular Speaker Normalization technique that can be implemented as a transformation of a spectrum frequency axis. We introduce in this document a new spectral transformation for Speaker Normalization. We use the Bilinear Transformation to introduce a new frequency warping resulting from a mapping of a prototype Band-Pass (BP) filter into a general BP filter. This new transformation called the Bandpass Transformation (BPT) offers two degrees of freedom enabling complex warpings of the frequency axis that are different from previous works with the Bilinear Transform. We then define a procedure to use BPT for Speaker Normalization based on the Nelder-Mead algorithm for the estimation of the BPT parameters. We present a detailed study of the performance of our new approach on two test sets with gender dependent and independent systems. Our results demonstrate clear improvements compared to standard methods used in VTL Normalization. A score compensation procedure is presented and results in further improvements of our results by refining our BPT parameter estimation

Combining vocal tract length normalization with hierarchial linear transformations

Recent research has demonstrated the effectiveness of vocal tract length normalization (VTLN) as a rapid adaptation technique for statistical parametric speech synthesis. VTLN produces speech with naturalness preferable to that of MLLR-based adaptation techniques, being much closer in quality to that generated by the original av-erage voice model. However with only a single parameter, VTLN captures very few speaker specific characteristics when compared to linear transform based adaptation techniques. This paper pro-poses that the merits of VTLN can be combined with those of linear transform based adaptation in a hierarchial Bayesian frame-work, where VTLN is used as the prior information. A novel tech-nique for propagating the gender information from the VTLN prior through constrained structural maximum a posteriori linear regres-sion (CSMAPLR) adaptation is presented. Experiments show that the resulting transformation has improved speech quality with better naturalness, intelligibility and improved speaker similarity. Index Terms — Statistical parametric speech synthesis, hidden Markov models, speaker adaptation, vocal tract length normaliza-tion, constrained structural maximum a posteriori linear regression 1

Speaker Normalization Using Cortical Strip Maps: A Neural Model for Steady State Vowel Identification

Auditory signals of speech are speaker-dependent, but representations of language meaning are speaker-independent. Such a transformation enables speech to be understood from different speakers. A neural model is presented that performs speaker normalization to generate a pitchindependent representation of speech sounds, while also preserving information about speaker identity. This speaker-invariant representation is categorized into unitized speech items, which input to sequential working memories whose distributed patterns can be categorized, or chunked, into syllable and word representations. The proposed model fits into an emerging model of auditory streaming and speech categorization. The auditory streaming and speaker normalization parts of the model both use multiple strip representations and asymmetric competitive circuits, thereby suggesting that these two circuits arose from similar neural designs. The normalized speech items are rapidly categorized and stably remembered by Adaptive Resonance Theory circuits. Simulations use synthesized steady-state vowels from the Peterson and Barney [J. Acoust. Soc. Am. 24, 175-184 (1952)] vowel database and achieve accuracy rates similar to those achieved by human listeners. These results are compared to behavioral data and other speaker normalization models.National Science Foundation (SBE-0354378); Office of Naval Research (N00014-01-1-0624

Speaker Normalization Using Cortical Strip Maps: A Neural Model for Steady State vowel Categorization

Auditory signals of speech are speaker-dependent, but representations of language meaning are speaker-independent. The transformation from speaker-dependent to speaker-independent language representations enables speech to be learned and understood from different speakers. A neural model is presented that performs speaker normalization to generate a pitch-independent representation of speech sounds, while also preserving information about speaker identity. This speaker-invariant representation is categorized into unitized speech items, which input to sequential working memories whose distributed patterns can be categorized, or chunked, into syllable and word representations. The proposed model fits into an emerging model of auditory streaming and speech categorization. The auditory streaming and speaker normalization parts of the model both use multiple strip representations and asymmetric competitive circuits, thereby suggesting that these two circuits arose from similar neural designs. The normalized speech items are rapidly categorized and stably remembered by Adaptive Resonance Theory circuits. Simulations use synthesized steady-state vowels from the Peterson and Barney [J. Acoust. Soc. Am. 24, 175-184 (1952)] vowel database and achieve accuracy rates similar to those achieved by human listeners. These results are compared to behavioral data and other speaker normalization models.National Science Foundation (SBE-0354378); Office of Naval Research (N00014-01-1-0624

Vocal Tract Length Normalization for Statistical Parametric Speech Synthesis

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

VTLN Adaptation for Statistical Speech Synthesis

The advent of statistical speech synthesis has enabled the unification of the basic techniques used in speech synthesis and recognition. Adaptation techniques that have been successfully used in recognition systems can now be applied to synthesis systems to improve the quality of the synthesized speech. The application of vocal tract length normalization (VTLN) for synthesis is explored in this paper. VTLN based adaptation requires estimation of a single warping factor, which can be accurately estimated from very little adaptation data and gives additive improvements over CMLLR adaptation. The challenge of estimating accurate warping factors using higher order features is solved by initializing warping factor estimation with the values calculated from lower order features

Study of Jacobian Normalization for VTLN

The divergence of the theory and practice of vocal tract length normalization (VTLN) is addressed, with particular emphasis on the role of the Jacobian determinant. VTLN is placed in a Bayesian setting, which brings in the concept of a prior on the warping factor. The form of the prior, together with acoustic scaling and numerical conditioning are then discussed and evaluated. It is concluded that the Jacobian determinant is important in VTLN, especially for the high dimensional features used in HMM based speech synthesis, and difficulties normally associated with the Jacobian determinant can be attributed to prior and scaling

VTLN-Based Rapid Cross-Lingual Adaptation for Statistical Parametric Speech Synthesis

Cross-lingual speaker adaptation (CLSA) has emerged as a new challenge in statistical parametric speech syn- thesis, with specific application to speech-to-speech translation. Recent research has shown that reasonable speaker similarity can be achieved in CLSA using maximum likelihood linear transformation of model parameters, but this method also has weaknesses due to the inherent mismatch caused by differing phonetic inventories of languages. In this paper, we propose that fast and effective CLSA can be made using vocal tract length normalization (VTLN), where strong constraints of the vocal tract warping function may actually help to avoid the most severe effects of the aforementioned mismatch. VTLN has a single parameter that warps spectrum. Using shifted or adapted pitch, VTLN can still achieve reasonable speaker similarity. We present our approach, VTLN-based CLSA, and evaluation results that support our proposal under the limitation that the voice identity and speaking style of a target speaker don’t diverge too far from that of the average voice model

A Comparative Study of Spectral Peaks Versus Global Spectral Shape as Invariant Acoustic Cues for Vowels

The primary objective of this study was to compare two sets of vowel spectral features, formants and global spectral shape parameters, as invariant acoustic cues to vowel identity. Both automatic vowel recognition experiments and perceptual experiments were performed to evaluate these two feature sets. First, these features were compared using the static spectrum sampled in the middle of each steady-state vowel versus features based on dynamic spectra. Second, the role of dynamic and contextual information was investigated in terms of improvements in automatic vowel classification rates. Third, several speaker normalizing methods were examined for each of the feature sets. Finally, perceptual experiments were performed to determine whether vowel perception is more correlated with formants or global spectral shape. Results of the automatic vowel classification experiments indicate that global spectral shape features contain more information than do formants. For both feature sets, dynamic features are superior to static features. Spectral features spanning a time interval beginning with the start of the on-glide region of the acoustic vowel segment and ending at the end of the off-glide region of the acoustic vowel segment are required for maximum vowel recognition accuracy. Speaker normalization of both static and dynamic features can also be used to improve the automatic vowel recognition accuracy. Results of the perceptual experiments with synthesized vowel segments indicate that if formants are kept fixed, global spectral shape can, at least for some conditions, be modified such that the synthetic speech token will be perceived according to spectral shape cues rather than formant cues. This result implies that overall spectral shape may be more important perceptually than the spectral prominences represented by the formants. The results of this research contribute to a fundamental understanding of the information-encoding process in speech. The signal processing techniques used and the acoustic features found in this study can also be used to improve the preprocessing of acoustic signals in the front-end of automatic speech recognition systems