We propose two novel techniques---stacking bottleneck features and minimum generation error (MGE) training criterion---to improve the performance of deep neural network (DNN)-based speech synthesis. The techniques address the related issues of frame-by-frame independence and ignorance of the relationship between static and dynamic features, within current typical DNN-based synthesis frameworks. Stacking bottleneck features, which are an acoustically informed linguistic representation, provides an efficient way to include more detailed linguistic context at the input. The MGE training criterion minimises overall output trajectory error across an utterance, rather than minimising the error per frame independently, and thus takes into account the interaction between static and dynamic features. The two techniques can be easily combined to further improve performance. We present both objective and subjective results that demonstrate the effectiveness of the proposed techniques. The subjective results show that combining the two techniques leads to significantly more natural synthetic speech than from conventional DNN or long short-term memory recurrent neural network systems.
Very substantial effort has been devoted to acoustic modelling in the hidden Markov model (HMM) speech synthesis framework. Amongst the many proposed techniques, we highlight just a few of the most influential. @cite_28 , a minimum generation error training criterion was proposed to address an inconsistency between training and generation criteria, and the lack of interaction between static and dynamic features during training. @cite_20 , the so-called trajectory HMM was proposed to explicitly model the relationships between static and dynamic features. As a complement to improving the acoustic model itself, enhancement techniques such as global variance @cite_36 and modulation spectrum enhancement @cite_19 aim to mitigate the lack of variation in generated parameter trajectories that results from using an incorrect acoustic model. Although such enhancement techniques do not reduce objective error (e.g., lower spectral distortion w.r.t. a natural speech reference), significant improvements in subjective naturalness are obtained. However, none of the above techniques address what is perhaps the most fundamental problem of HMM-based speech synthesis: across-context averaging via decision tree clustering, which has been identified as a major contributing factor to reduced naturalness @cite_17 .
@cite_28 @cite_36 @cite_19 @cite_20 @cite_17
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A statistical speech synthesis system based on the hidden Markov model (HMM) was recently proposed. In this system, spectrum, excitation, and duration of speech are modeled simultaneously by context-dependent HMMs, and speech parameter vector sequences are generated from the HMMs themselves. This system defines a speech synthesis problem in a generative model framework and solves it based on the maximum likelihood (ML) criterion. However, there is an inconsistency: although state duration probability density functions (PDFs) are explicitly used in the synthesis part of the system, they have not been incorporated into its training part. This inconsistency can make the synthesized speech sound less natural. In this paper, we propose a statistical speech synthesis system based on a hidden semi-Markov model (HSMM), which can be viewed as an HMM with explicit state duration PDFs. The use of HSMMs can solve the above inconsistency because we can incorporate the state duration PDFs explicitly into both the synthesis and the training parts of the system. Subjective listening test results show that use of HSMMs improves the reported naturalness of synthesized speech. In this paper, we propose an HMM trajectory-guided, real image sample concatenation approach to photo-realistic talking head synthesis. An audio-visual database of a person is recorded first for training a statistical Hidden Markov Model (HMM) of Lips movement. The HMM is then used to generate the dynamic trajectory of lips movement for given speech signals in the maximum probability sense. The generated trajectory is then used as a guide to select, from the original training database, an optimal sequence of lips images which are then stitched back to a background head video. We also propose a minimum generation error (MGE) training method to refine the audio-visual HMM to improve visual speech trajectory synthesis. Compared with the traditional maximum likelihood (ML) estimation, the proposed MGE training explicitly optimizes the quality of generated visual speech trajectory, where the audio-visual HMM modeling is jointly refined by using a heuristic method to find the optimal state alignment and a probabilistic descent algorithm to optimize the model parameters under the MGE criterion. In objective evaluation, compared with the ML-based method, the proposed MGE-based method achieves consistent improvement in the mean square error reduction, correlation increase, and recovery of global variance. For as short as 20 min recording of audio video footage, the proposed system can synthesize a highly photo-realistic talking head in sync with the given speech signals (natural or TTS synthesized). This system won the first place in the A V consistency contest in LIPS Challenge, perceptually evaluated by recruited human subjects. In the present paper, a trajectory model, derived from a hidden Markov model (HMM) by imposing explicit relationships between static and dynamic feature vector sequences, is developed and evaluated. The derived model, named a trajectory HMM, can alleviate two limitations of the standard HMM, which are (i) piece-wise constant statistics within a state and (ii) conditional independence assumption of state output probabilities, without increasing the number of model parameters. In the present paper, a Viterbi-type training algorithm based on the maximum likelihood criterion is also derived. The performance of the trajectory HMM was evaluated both in speech recognition and synthesis. In a speaker-dependent continuous speech recognition experiment, the trajectory HMM achieved an error reduction over the corresponding standard HMM. Subjective listening test results showed that the introduction of the trajectory HMM improved the naturalness of synthetic speech. This paper describes a novel parameter generation algorithm for an HMM-based speech synthesis technique. The conventional algorithm generates a parameter trajectory of static features that maximizes the likelihood of a given HMM for the parameter sequence consisting of the static and dynamic features under an explicit constraint between those two features. The generated trajectory is often excessively smoothed due to the statistical processing. Using the over-smoothed speech parameters usually causes muffled sounds. In order to alleviate the over-smoothing effect, we propose a generation algorithm considering not only the HMM likelihood maximized in the conventional algorithm but also a likelihood for a global variance (GV) of the generated trajectory. The latter likelihood works as a penalty for the over-smoothing, i.e., a reduction of the GV of the generated trajectory. The result of a perceptual evaluation demonstrates that the proposed algorithm causes considerably large improvements in the naturalness of synthetic speech. Acoustic models used in hidden Markov model neural-network (HMM NN) speech recognition systems are usually trained with a frame-based cross-entropy error criterion. In contrast, Gaussian mixture HMM systems are discriminatively trained using sequence-based criteria, such as minimum phone error or maximum mutual information, that are more directly related to speech recognition accuracy. This paper demonstrates that neural-network acoustic models can be trained with sequence classification criteria using exactly the same lattice-based methods that have been developed for Gaussian mixture HMMs, and that using a sequence classification criterion in training leads to considerably better performance. A neural network acoustic model with 153K weights trained on 50 hours of broadcast news has a word error rate of 34.0 on the rt04 English broadcast news test set. When this model is trained with the state-level minimum Bayes risk criterion, the rt04 word error rate is 27.7 . In HMM-based speech synthesis, there are two issues critical related to the MLE-based HMM training: the inconsistency between training and synthesis, and the lack of mutual constraints between static and dynamic features. In this paper, we propose minimum generation error (MGE) based HMM training method to solve these two issues. In this method, an appropriate generation error is defined, and the HMM parameters are optimized by using the generalized probabilistic descent (GPD) algorithm, with the aims to minimize the generation errors. From the experimental results, the generation errors were reduced after the MGE-based HMM training, and the quality of synthetic speech is improved. In this paper, we propose an HMM trajectory-guided, real image sample concatenation approach to photo-real talking head synthesis. It renders a smooth and natural video of articulators in sync with given speech signals. An audio-visual database is used to train a statistical Hidden Markov Model (HMM) of lips movement first and the trained model is then used to generate a visual parameter trajectory of lips movement for given speech signals, all in the maximum likelihood sense. The HMM generated trajectory is then used as a guide to select, in the original training database, an optimal sequence of mouth images which are then stitched back to a background head video. The whole procedure is fully automatic and data driven. With an audio video footage as short as 20 minutes from a speaker, the proposed system can synthesize a highly photo-real video in sync with the given speech signals. This system won the FIRST place in the Audio-Visual match contest in LIPS2009 Challenge, which was perceptually evaluated by recruited human subjects. Index Terms: visual speech synthesis, photo-real, talking head, trajectory-guided In hybrid hidden Markov model artificial neural networks (HMM ANN) automatic speech recognition (ASR) system, the phoneme class conditional probabilities are estimated by first extracting acoustic features from the speech signal based on prior knowledge such as, speech perception or and speech production knowledge, and, then modeling the acoustic features with an ANN. Recent advances in machine learning techniques, more specifically in the field of image processing and text processing, have shown that such divide and conquer strategy (i.e., separating feature extraction and modeling steps) may not be necessary. Motivated from these studies, in the framework of convolutional neural networks (CNNs), this paper investigates a novel approach, where the input to the ANN is raw speech signal and the output is phoneme class conditional probability estimates. On TIMIT phoneme recognition task, we study different ANN architectures to show the benefit of CNNs and compare the proposed approach against conventional approach where, spectral-based feature MFCC is extracted and modeled by a multilayer perceptron. Our studies show that the proposed approach can yield comparable or better phoneme recognition performance when compared to the conventional approach. It indicates that CNNs can learn features relevant for phoneme classification automatically from the raw speech signal. This paper describes, and evaluates on a large scale, the lattice based framework for discriminative training of large vocabulary speech recognition systems based on Gaussian mixture hidden Markov models (HMMs). This paper concentrates on the maximum mutual information estimation (MMIE) criterion which has been used to train HMM systems for conversational telephone speech transcription using up to 265 hours of training data. These experiments represent the largest-scale application of discriminative training techniques for speech recognition of which the authors are aware. Details are given of the MMIE lattice-based implementation used with the extended Baum-Welch algorithm, which makes training of such large systems computationally feasible. Techniques for improving generalization using acoustic scaling and weakened language models are discussed. The overall technique has allowed the estimation of triphone and quinphone HMM parameters which has led to significant reductions in word error rate for the transcription of conversational telephone speech relative to our best systems trained using maximum likelihood estimation (MLE). This is in contrast to some previous studies, which have concluded that there is little benefit in using discriminative training for the most difficult large vocabulary speech recognition tasks. The lattice MMIE-based discriminative training scheme is also shown to out-perform the frame discrimination technique. Various properties of the lattice-based MMIE training scheme are investigated including comparisons of different lattice processing strategies (full search and exact-match) and the effect of lattice size on performance. Furthermore a scheme based on the linear interpolation of the MMIE and MLE objective functions is shown to reduce the danger of over-training. It is shown that HMMs trained with MMIE benefit as much as MLE-trained HMMs from applying model adaptation using maximum likelihood linear regression (MLLR). This has allowed the straightforward integration of MMIE-trained HMMs into complex multi-pass systems for transcription of conversational telephone speech and has contributed to our MMIE-trained systems giving the lowest word error rates in both the 2000 and 2001 NIST Hub5 evaluations. Hidden Markov model speech recognition systems typically use Gaussian mixture models to estimate the distributions of decorrelated acoustic feature vectors that correspond to individual subword units. By contrast, hybrid connectionist-HMM systems use discriminatively-trained neural networks to estimate the probability distribution among subword units given the acoustic observations. In this work we show a large improvement in word recognition performance by combining neural-net discriminative feature processing with Gaussian-mixture distribution modeling. By training the network to generate the subword probability posteriors, then using transformations of these estimates as the base features for a conventionally-trained Gaussian-mixture based system, we achieve relative error rate reductions of 35 or more on the multicondition Aurora noisy continuous digits task. This paper presents a new spectral modeling method for statistical parametric speech synthesis. In the conventional methods, high-level spectral parameters, such as mel-cepstra or line spectral pairs, are adopted as the features for hidden Markov model (HMM)-based parametric speech synthesis. Our proposed method described in this paper improves the conventional method in two ways. First, distributions of low-level, un-transformed spectral envelopes (extracted by the STRAIGHT vocoder) are used as the parameters for synthesis. Second, instead of using single Gaussian distribution, we adopt the graphical models with multiple hidden variables, including restricted Boltzmann machines (RBM) and deep belief networks (DBN), to represent the distribution of the low-level spectral envelopes at each HMM state. At the synthesis time, the spectral envelopes are predicted from the RBM-HMMs or the DBN-HMMs of the input sentence following the maximum output probability parameter generation criterion with the constraints of the dynamic features. A Gaussian approximation is applied to the marginal distribution of the visible stochastic variables in the RBM or DBN at each HMM state in order to achieve a closed-form solution to the parameter generation problem. Our experimental results show that both RBM-HMM and DBN-HMM are able to generate spectral envelope parameter sequences better than the conventional Gaussian-HMM with superior generalization capabilities and that DBN-HMM and RBM-HMM perform similarly due possibly to the use of Gaussian approximation. As a result, our proposed method can significantly alleviate the over-smoothing effect and improve the naturalness of the conventional HMM-based speech synthesis system using mel-cepstra. The context-independent deep belief network (DBN) hidden Markov model (HMM) hybrid architecture has recently achieved promising results for phone recognition. In this work, we propose a context-dependent DBN-HMM system that dramatically outperforms strong Gaussian mixture model (GMM)-HMM baselines on a challenging, large vocabulary, spontaneous speech recognition dataset from the Bing mobile voice search task. Our system achieves absolute sentence accuracy improvements of 5.8 and 9.2 over GMM-HMMs trained using the minimum phone error rate (MPE) and maximum likelihood (ML) criteria, respectively, which translate to relative error reductions of 16.0 and 23.2 . This paper gives a general overview of hidden Markov model (HMM)-based speech synthesis, which has recently been demonstrated to be very effective in synthesizing speech. The main advantage of this approach is its flexibility in changing speaker identities, emotions, and speaking styles. This paper also discusses the relation between the HMM-based approach and the more conventional unit-selection approach that has dominated over the last decades. Finally, advanced techniques for future developments are described. Hidden Markov modeling (HMM) techniques have been used successfully for connected speech recognition in the last several years. In the traditional HMM algorithms, the probability of duration of a state decreases exponentially with time which is not appropriate for representing the temporal structure of speech. Non-parametric modeling of duration using semi-Markov chains does accomplish the task with a large increase in the computational complexity. Applying a postprocessing state duration penalty after Viterbi decoding adds very little computation but does not affect the forward recognition path. The authors present a way of modeling state durations in HMM using time-dependent state transitions. This inhomogeneous HMM (IHMM) does increase the computation by a small amount but reduces recognition error rates by 14-25 . Also, a suboptimal implementation of this scheme that requires no more computation than the traditional HMM is presented which also has reduced errors by 14-22 on a variety of databases. > In this paper, we propose a postfilter to compensate modulation spectrum in HMM-based speech synthesis. In order to alleviate over-smoothing effects which is a main cause of quality degradation in HMM-based speech synthesis, it is necessary to consider features that can capture over-smoothing. Global Variance (GV) is one well-known example of such a feature, and the effectiveness of parameter generation algorithm considering GV have been confirmed. However, the quality gap between natural speech and synthetic speech is still large. In this paper, we introduce the Modulation Spectrum (MS) of speech parameter trajectory as a new feature to effectively capture the over-smoothing effect, and we propose a postfilter based on the MS. The MS is represented as a power spectrum of the parameter trajectory. The generated speech parameter sequence is filtered to ensure that its MS has a pattern similar to natural speech. Experimental results show quality improvements when the proposed methods are applied to spectral and F 0 components, compared with conventional methods considering GV. The use of Deep Belief Networks (DBN) to pretrain Neural Networks has recently led to a resurgence in the use of Artificial Neural Network Hidden Markov Model (ANN HMM) hybrid systems for Automatic Speech Recognition (ASR). In this paper we report results of a DBN-pretrained context-dependent ANN HMM system trained on two datasets that are much larger than any reported previously with DBN-pretrained ANN HMM systems 5870 hours of Voice Search and 1400 hours of YouTube data. On the first dataset, the pretrained ANN HMM system outperforms the best Gaussian Mixture Model Hidden Markov Model (GMM HMM) baseline, built with a much larger dataset by 3.7 absolute WER, while on the second dataset, it outperforms the GMM HMM baseline by 4.7 absolute. Maximum Mutual Information (MMI) fine tuning and model combination using Segmental Conditional Random Fields (SCARF) give additional gains of 0.1 and 0.4 on the first dataset and 0.5 and 0.9 absolute on the second dataset. Even the best statistical parametric speech synthesis systems do not achieve the naturalness of good unit selection. We investigated possible causes of this. By constructing speech signals that lie in between natural speech and the output from a complete HMM synthesis system, we investigated various effects of modelling. We manipulated the temporal smoothness and the variance of the spectral parameters to create stimuli, then presented these to listeners alongside natural and vocoded speech, as well as output from a full HMM-based text-to-speech system and from an idealised ‘pseudo-HMM’. All speech signals, except the natural waveform, were created using vocoders employing one of two popular spectral parameterisations: Mel-Cepstra or Mel-Line Spectral Pairs. Listeners made ‘same or different’ pairwise judgements, from which we generated a perceptual map using Multidimensional Scaling. We draw conclusions about which aspects of HMM synthesis are limiting the naturalness of the synthetic speech. Recently, the hybrid deep neural network (DNN)- hidden Markov model (HMM) has been shown to significantly improve speech recognition performance over the conventional Gaussian mixture model (GMM)-HMM. The performance improvement is partially attributed to the ability of the DNN to model complex correlations in speech features. In this paper, we show that further error rate reduction can be obtained by using convolutional neural networks (CNNs). We first present a concise description of the basic CNN and explain how it can be used for speech recognition. We further propose a limited-weight-sharing scheme that can better model speech features. The special structure such as local connectivity, weight sharing, and pooling in CNNs exhibits some degree of invariance to small shifts of speech features along the frequency axis, which is important to deal with speaker and environment variations. Experimental results show that CNNs reduce the error rate by 6 -10 compared with DNNs on the TIMIT phone recognition and the voice search large vocabulary speech recognition tasks. Summary form only given. The paper concerns the development of a system for the recognition of a context or an environment based on acoustic information only. Our system uses Mel-frequency cepstral coefficients and their derivatives as features, and continuous density hidden Markov models (HMM) as acoustic models. We evaluate different model topologies and training methods for HMMs and show that discriminative training can yield a 10 reduction in error rate compared to maximum-likelihood training. A listening test is made to study the human accuracy in the task and to obtain a base-line for the assessment of the performance of the system. Direct comparison to human performance indicates that the system performs somewhat worse than human subjects do in the recognition of 18 everyday contexts and almost comparably in recognizing six higher level categories. We propose a coupled hidden Markov model (CHMM) approach to video-realistic speech animation, which realizes realistic facial animations driven by speaker independent continuous speech. Different from hidden Markov model (HMM)-based animation approaches that use a single-state chain, we use CHMMs to explicitly model the subtle characteristics of audio-visual speech, e.g., the asynchrony, temporal dependency (synchrony), and different speech classes between the two modalities. We derive an expectation maximization (EM)-based A V conversion algorithm for the CHMMs, which converts acoustic speech into decent facial animation parameters. We also present a video-realistic speech animation system. The system transforms the facial animation parameters to a mouth animation sequence, refines the animation with a performance refinement process, and finally stitches the animated mouth with a background facial sequence seamlessly. We have compared the animation performance of the CHMM with the HMMs, the multi-stream HMMs and the factorial HMMs both objectively and subjectively. Results show that the CHMMs achieve superior animation performance. The ph-vi-CHMM system, which adopts different state variables (phoneme states and viseme states) in the audio and visual modalities, performs the best. The proposed approach indicates that explicitly modelling audio-visual speech is promising for speech animation.