I believe that the best way to get started(in any field) is by watching a few introductory video lectures, reading some PhD and Masters dissertations related to the field, some(exceptional?) papers and journals. I have populated this page hence with these, and ofcourse from the people I believe are the best(non-exhaustive list) in the business. I also plan to add my interpretations of everything on this page soon !!
Current state-of-the-art text-to-speech systems produce intelligible speech but lack the prosody of natural utterances. Building better models of prosody involves development of prosodically rich speech databases. However, development of such speech databases requires a large amount of effort and time. An alternative is to exploit story style monologues (long speech files) in audio books. These monologues already encapsulate rich prosody including varied intonation contours, pitch accents and phrasing patterns. Thus, audio books act as excellent candidates for building prosodic models and natural sounding synthetic voices. The processing of such audio books poses several challenges including segmentation of long speech files, detection of mispronunciations, extraction and evaluation of representations of prosody. In this thesis, we address the issues of segmentation of long speech files, capturing prosodic phrasing patterns of a speaker, and conversion of speaker characteristics. Techniques developed to address these issues include – text-driven and speech-driven methods for segmentation of long speech files; an unsupervised algorithm for learning speaker-specific phrasing patterns and a voice conversion method by modeling target speaker characteristics.
In recent years, the most popular acoustic model in automatic speech recognition (ASR) and text-to-speech synthesis (TTS) is a hidden Markov model (HMM), due to its ease of implementation and modeling flexibility. However, a number of limitations for modeling sequences of speech spectra using the HMM have been pointed out, such as i) piece-wise constant statistics within a state and ii) conditional independence assumption of state output probabilities. To overcome these shortcomings, a variety of alternative acoustic models have been proposed. Although these models can improve model accuracy and speech recognition performance, they generally require an increase in the number of model parameters. In contrast, dynamic features can also enhance performances of HMM-based speech recognizers and has been widely adopted. It can be viewed as a simple mechanism to capture time dependencies in the HMM. However, this approach is mathematically improper in the sense of statistical modeling. Generally, the dynamic features are calculated as regression coefficients from their neighboring static features. Therefore, relationships between the static and dynamic features are deterministic. However, these relationships are ignored and the static and dynamic features are modeled as independent statistical variables in the HMM framework. Ignoring these interdependencies allows inconsistency between the static and dynamic features when the HMM is used as a generative model in the obvious way. In the present dissertation, a novel acoustic model, named a trajectory HMM, is described. This model is derived from the HMM whose state output vector includes both static and dynamic features. By imposing explicit relationships between the static and dynamic features, the HMM is naturally translated into a trajectory model. The above inconsistency and limitations of the HMM can be alleviated by the trajectory HMM. Furthermore, parameterization of the trajectory HMM is completely the same as that of the HMM with the same model topology. Therefore, any additional parameters are not required. In the present dissertation, model training algorithms based on a Viterbi approximation and a Markov chain Monte Carlo (MCMC) method and a search algorithm based on a delayed decision strategy are also derived. Results of continuous speech recognition and speech synthesis experiments show that the trajectory HMM can improve the performance both of speech recognizers and synthesizers.
Text-to-Speech (TTS) is a useful technology that converts any text into a speech signal. It can be utilized for various purposes, e.g. car navigation, announcements in railway stations, response services in telecommunications, and e-mail reading. Corpus-based TTS makes it possible to dramatically improve the naturalness of synthetic speech compared with the early TTS. However, no general-purpose TTS has been developed that can consistently synthesize sufficiently natural speech. Furthermore, there is not yet enough flexibility in corpus- based TTS. This thesis addresses two problems in speech synthesis. One is how to improve the naturalness of synthetic speech in corpus-based TTS. The other is how to improve control of speaker individuality in order to achieve more flexible speech synthesis. To deal with the former problem, we focus on two factors: (1) an algorithm for selecting the most appropriate synthesis units from a speech corpus, and (2) an evaluation measure for selecting the synthesis units. Moreover, we focus on a voice conversion technique to control speaker individuality to deal with the latter problem. Since various vowel sequences appear frequently in Japanese, it is not realistic to prepare long units that include all possible vowel sequences to avoid vowel-to-vowel concatenation, which often produces auditory discontinuity. In order to address this problem, we propose a novel segment selection algorithm based on both phoneme and diphone units that does not avoid concatenation of vowel sequences but alleviates the resulting discontinuity. Experiments testing concatenation of vowel sequences clarify that better segments can be selected by considering concatenations not only at phoneme boundaries but also at vowel centers. Moreover, the results of perceptual experiments show that speech synthesized using the proposed algorithm has better naturalness than that using the conventional algorithms. A cost is established as a measure for selecting the optimum waveform segments from a speech corpus. In order to achieve high-quality segment selection for concatenative TTS, it is important to utilize a cost that corresponds to perceptual characteristics. We first clarify the correspondence of the cost to the perceptual scores and then evaluate various functions to integrate local costs capturing the degradation of naturalness in individual segments. From the results of perceptual experiments, we find a novel cost that takes into account not only the degradation of naturalness over the entire synthetic speech but also the local degradation. We also clarify that the naturalness of synthetic speech can be slightly improved by utilizing this cost and investigate the effect of using this cost for segment selection. We improve the voice conversion algorithm based on the Gaussian Mixture Model (GMM), which is a conventional statistical voice conversion algorithm. The GMM-based algorithm can convert speech features continuously using the correlations between source and target features. However, the quality of the converted speech is degraded because the converted spectrum is excessively smoothed by the statistical averaging operation. To overcome this problem, we propose a novel voice conversion algorithm that incorporates Dynamic Frequency Warping (DFW) technique. The experimental results reveal that the proposed algorithm can synthesize speech with a higher quality while maintaining equal conversion- accuracy for speaker individuality compared with the GMM-based algorithm.
This thesis describes a novel speech synthesis framework “Average-Voice-based Speech Synthesis.” By using the speech synthesis framework, synthetic speech of arbitrary target speakers can be obtained robustly and steadily even if speech samples available for the target speaker are very small. This speech synthesis framework consists of speaker normalization algorithm for the parameter clustering, speaker normalization algorithm for the parameter estimation, the transformation/adaptation part, and modification part of the rough transformation. In the parameter clustering using decision-tree-based context clustering techniques for average voice model, the nodes of the decision tree do not always have training data of all speakers, and some nodes have data from only one speaker. This speaker-biased node causes degradation of quality of average voice and synthetic speech after speaker adaptation, especially in prosody. Therefore, we firstly propose a new context clustering technique, named 'shared-decision-tree-based context clustering' to overcome this problem. Using this technique, every node of the decision tree always has training data from all speakers included in the training speech database. As a result, we can construct decision tree common to all training speakers and each distribution of the node always reflects the statistics of all speakers. However, when training data of each training speaker differs widely, the distributions of the node often have bias depending on speaker and/or gender and this will degrade the quality of synthetic speech. Therefore, we incorporate 'speaker adaptive training' into the parameter estimation procedure of average voice model to reduce the influence of speaker dependence. In the speaker adaptive training, the speaker difference between training speaker’s voice and average voice is assumed to be expressed as a simple linear regression function of mean vector of the distribution and a canonical average voice model is estimated using the assumption. In speaker adaptation for speech synthesis, it is desirable to convert both voice characteristics and prosodic features such as F0 and phone duration. Therefore, we utilize a framework of 'hidden semi-Markov model' (HSMM) which is an HMM having explicit state duration distributions and we propose an HSMM-based model adaptation algorithm to simultaneously transform both state output and state duration distributions. Furthermore, we also propose an HSMM-based speaker adaptive training algorithm to normalize both state output and state duration distributions of average voice model at the same time. Finally, we explore several speaker adaptation algorithms to transform more effectively the average voice model into the target speaker’s model when the adaptation data for the target speaker is limited. Furthermore, we adopt “MAP (Maximum A Posteriori) modification” to upgrade the estimation for the distributions having sufficient amount of speech data. When sufficient amount of the adaptation data is available, the MAP modification theoretically matches the ML estimation. As a result, it is thought that we do not need to choose the modeling strategy depending on the amount of speech data and we would accomplish the consistent method to synthesize speech in the unified way for arbitrary amount of the speech data.