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

Bayesian Speaker Adaptation Based on a New Hierarchical Probabilistic Model

In this paper, a new hierarchical Bayesian speaker adaptation method called HMAP is proposed that combines the advantages of three conventional algorithms, maximum a posteriori (MAP), maximum-likelihood linear regression (MLLR), and eigenvoice, resulting in excellent performance across a wide range of adaptation conditions. The new method efficiently utilizes intra-speaker and inter-speaker correlation information through modeling phone and speaker subspaces in a consistent hierarchical Bayesian way. The phone variations for a specific speaker are assumed to be located in a low-dimensional subspace. The phone coordinate, which is shared among different speakers, implicitly contains the intra-speaker correlation information. For a specific speaker, the phone variation, represented by speaker-dependent eigenphones, are concatenated into a supervector. The eigenphone supervector space is also a low dimensional speaker subspace, which contains inter-speaker correlation information. Using principal component analysis (PCA), a new hierarchical probabilistic model for the generation of the speech observations is obtained. Speaker adaptation based on the new hierarchical model is derived using the maximum a posteriori criterion in a top-down manner. Both batch adaptation and online adaptation schemes are proposed. With tuned parameters, the new method can handle varying amounts of adaptation data automatically and efficiently. Experimental results on a Mandarin Chinese continuous speech recognition task show good performance under all testing conditions

Robustness issues in a data-driven spoken language understanding system

Robustness is a key requirement in spoken language understanding (SLU) systems. Human speech is often ungrammatical and ill-formed, and there will frequently be a mismatch between training and test data. This paper discusses robustness and adaptation issues in a statistically-based SLU system which is entirely data-driven. To test robustness, the system has been tested on data from the Air Travel Information Service (ATIS) domain which has been artificially corrupted with varying levels of additive noise. Although the speech recognition performance degraded steadily, the system did not fail catastrophically. Indeed, the rate at which the end-to-end performance of the complete system degraded was significantly slower than that of the actual recognition component. In a second set of experiments, the ability to rapidly adapt the core understanding component of the system to a different application within the same broad domain has been tested. Using only a small amount of training data, experiments have shown that a semantic parser based on the Hidden Vector State (HVS) model originally trained on the ATIS corpus can be straightforwardly adapted to the somewhat different DARPA Communicator task using standard adaptation algorithms. The paper concludes by suggesting that the results presented provide initial support to the claim that an SLU system which is statistically-based and trained entirely from data is intrinsically robust and can be readily adapted to new applications

Generalization of Extended Baum-Welch Parameter Estimation for Discriminative Training and Decoding

We demonstrate the generalizability of the Extended Baum-Welch (EBW) algorithm not only for HMM parameter estimation but for decoding as well.\ud We show that there can exist a general function associated with the objective function under EBW that reduces to the well-known auxiliary function used in the Baum-Welch algorithm for maximum likelihood estimates.\ud We generalize representation for the updates of model parameters by making use of a differentiable function (such as arithmetic or geometric\ud mean) on the updated and current model parameters and describe their effect on the learning rate during HMM parameter estimation. Improvements on speech recognition tasks are also presented here

On adaptive decision rules and decision parameter adaptation for automatic speech recognition

Recent advances in automatic speech recognition are accomplished by designing a plug-in maximum a posteriori decision rule such that the forms of the acoustic and language model distributions are specified and the parameters of the assumed distributions are estimated from a collection of speech and language training corpora. Maximum-likelihood point estimation is by far the most prevailing training method. However, due to the problems of unknown speech distributions, sparse training data, high spectral and temporal variabilities in speech, and possible mismatch between training and testing conditions, a dynamic training strategy is needed. To cope with the changing speakers and speaking conditions in real operational conditions for high-performance speech recognition, such paradigms incorporate a small amount of speaker and environment specific adaptation data into the training process. Bayesian adaptive learning is an optimal way to combine prior knowledge in an existing collection of general models with a new set of condition-specific adaptation data. In this paper, the mathematical framework for Bayesian adaptation of acoustic and language model parameters is first described. Maximum a posteriori point estimation is then developed for hidden Markov models and a number of useful parameters densities commonly used in automatic speech recognition and natural language processing.published_or_final_versio

Synthesis using speaker adaptation from speech recognition DB

This paper deals with the creation of multiple voices from a Hidden Markov Model based speech synthesis system (HTS). More than 150 Catalan synthetic voices were built using Hidden Markov Models (HMM) and speaker adaptation techniques. Training data for building a Speaker-Independent (SI) model were selected from both a general purpose speech synthesis database (FestCat;) and a database design ed for training Automatic Speech Recognition (ASR) systems (Catalan SpeeCon database). The SpeeCon database was also used to adapt the SI model to different speakers. Using an ASR designed database for TTS purposes provided many different amateur voices, with few minutes of recordings not performed in studio conditions. This paper shows how speaker adaptation techniques provide the right tools to generate multiple voices with very few adaptation data. A subjective evaluation was carried out to assess the intelligibility and naturalness of the generated voices as well as the similarity of the adapted voices to both the original speaker and the average voice from the SI model.Peer ReviewedPostprint (published version