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.